RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
Authors: Zhengkang Guo
Wenhao Liu
Mingchen Xie
Jingwen Xu
Zisu Huang
Muzhao Tian
Jianhan Xu
Yuanzhe Shen
Qi Qian
Muling Wu
Xiaohua Wang
Heda Wang
Yao Hu
Changze Lv
Xuanjing Huang
Xiaoqing Zheng
Venue: ICLR 2026 Poster (2026)
Keywords: LLM
Complex Instruction Following
Data synthesis
Reinforement Learning
Primary area: generative models
TL;DR: We propose an efficient method for synthesizing high-quality data to enhance the complex instruction-following capabilities of large language models (LLMs).

Abstract: Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficiency in crafting sophisticated prompts. However, as the number of explicitly stated requirements increases (particularly more than $10$ constraints), LLMs often struggle to accurately follow such complex instructions, which limits their applicability in complex real-world scenarios. To the best of our knowledge, existing datasets do not exceed 10 constraints per instance. To address this challenge, we propose RECAST, an efficient and scalable framework for synthesizing datasets where each example incorporates far more constraints than those in existing benchmarks, aiming to challenge and extend the boundaries of models’ ability to follow complex instructions. These constraints are extracted from real-world prompt-response pairs to ensure practical relevance. Using this framework, we construct RECAST-$30$K, a large-scale, high-quality dataset comprising $30$k instances spanning $19$ constraint types. Experimental results demonstrate that models fine-tuned on RECAST-30K substantially improve in following complex instructions while maintaining their general capabilities without degradation. Moreover, RECAST enables automatic verification of constraint satisfaction via rule-based validators for quantitative constraints and LLM-based validators for qualitative ones, the verifiability provided by RECAST enables the design of reward functions for reinforcement learning, which further boosts model performance on complex and challenging tasks.

Published as a conference paper ICLR 2026
RECAST: EXPANDING THE BOUNDARIES OF LLMs'
COMPLEX INSTRUCTION FOLLOWING WITH MULTI-CONSTRAIN DATA
Zhengkang Guo
Wenhao L Mingchen Xie
Jingwen Xu'
Zisu Huang'
Muzhao Tian'
Jianhan Xu² Yuanzhe Shen' Qi Qian'
Muling Wu' Xiaohua Wang'
Changze Ly
HeDa Wang²
Hu Yao² Xiaoqing Zheng"
Xuanjing Huang'
College of Computer Science and Artificial Intelligence, Fudan University Xiiohhngghhu Inc.
{ (zkguo24, whliu22) @m. fudan. edu. on
chengxqffudan edu. on
ABSTRACT
Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficiency in crafting sophisticated prompts. However, as the number of explicitly stated requirements increases (particularly more than 10 constraints). LLMS often struggle accurately follow such complex instruc- tions, which limits their applicability in complex real-world scenarios. To the best of our knowledge, existing datasets do not exceed 10 constraints per instance. To address this chal- lenge, we propose RECAST, an efficient and scalable framework for synthesizing datasets where each example incorporates far more constraints than those in existing benchmarks, aiming to challenge and extend the boundaries of models' ability to follow complex in- structions. These constraints are extracted from real-world prompt-response pairs to ensure practical relevance. Using this framework, we construct RECAST-30K, a large-scale, high- quality dataset comprising 30k instances spanning 19 constraint types. Experimental results demonstrate that models fine-tuned on RECAST-30K substantially improve in following complex instructions while maintaining their general capabilities without degradation. Moreover, RECAST enables automatic verification of constraint satisfaction via rule-based validators for quantitative constraints and LLMM-bsed validators for qualitative ones, the verifiability provided by RECAST enables the design of reward functions for reinforcement learning, which further boosts model performance on complex and challenging tasks. Code and data available httpt:t///// /ttp::////t/ubbb/// com Thekey 756/66766
INTRODUCTION
Large language models (LLMs) have demonstrated remarkable capabilities in solving various NLP tasks
(Ahn et al.
2024
Chakrabarty et al. 2024 Kómme-Roddrguee" & Williams) 2023 Huang & Chang 2023
Jiang et al.
Zheng et al. 2023) and are widely used in practical scenarios (Gu et al. 2024 Liu et al. 2024 Sun 2023: hirunavukarasu et al., 2023). However, they still struggle with complex
2024a Zhao et al. 2023
multi-constraint scenarios that are prevalent in real-world applications, such constitutional AI systems
requiring adherence to multiple principles simultaneously (Bai et al. 2022) Findeis et al. 2025 Kundu et al.
2023
Kyrychenko al. 2025). and enterprise assistants managing detailed business rules (Michele et
2025
Grohs et al. 2023 Robino 2025).
"These authors contributed equally. Corresponding author.

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Published as a conference paper ICLR 2026
Instruction]
Chuce peesseck vy
Role Setting: Senior English-law M&A attorney (15 years)
WiR scce wil wolaaaa by 2e0- woateaelaaaaaao... esshaatio
Draft a rom-binding English Term Sheet for UK BuyerCo to acquire 100 % of Gemany-based TargetCo.
Constraints:
Manguage Plain modern businese English.
Blbetualty-Backgruuue Info: MAU text must match Annex A verbatim.
10Keyword: Goveming law English law only
Snyywerd-Numericlll Disputes: LCIA, London seat, single arbitrator,
12.Keyword Tone: Header line "STRICTLY CONFIDENTIAL" dutt perpetual.
Reesoose
STRICTE.Y STRICTLYYCOFIDEEN CONFIDENTIAL, Now-Rinding Term Sheet
Pricr EUR
equity.
umiccaty Level
Liability Cap 10% of Purchase Price (fmod excluded) (MAE diange that materally adversely sffects Seller's business."
Inctruct SPT
RUVC
Governing Law State of Delaware, USA
Averase Score
Diepute Resolution LCTA arbitration, London, ihree arbitrators Confidentiality Perpetual.
Verification
Extracts
Constraints
Analysis
MAR. *.... affects Seller's business" Backgrouu Info faols
Governing
Keyword
Used U.S. instead U.K.
London sent, single arbittrator
Key
Three arbitrators instead of onee
Base Model
Figure I: Challenges in complex instruction following and improved performance via RECAST- Left: Real-world examples illustrating how LLMs fail to follow complex instructions. Top-right: Performance degradation of LLMs as the number of constraints increases. Bottom-right: Comparison of instruction- following performance between models fine-tuned RECAST and their corresponding Instruct variants (e.g., Qwen2.5-7B-Insttuu.
Recently, LLM-based autonomous agents have attracted increasing attention from both academia and industry. Surveys such as Wang et al. (2024b) highlight their tremendous potential in social sciences, natural sciences, and engineering, which fundamentally relies on the ability LLMs to follow complex instructions. As shown in Figure I when instruction complexity increases, with multiple explicit constraints in single prompt, even advanced models like GPT-4o (Achiam et al.] 2023) show marked performance degradation, which has been emphasized in multiple benchmarks (Ferraz al. 2024 Jiang et al. 2024bb Qin al. 2024 Wen et al.) 2024). Therefore, advancing the complex Instruction-folowiin capabilities of L.Ms is crucial for promoting their practical deployment.
Although prior studies have explored methods to evaluate and improve the instruction-following abilities of LLMs, existing research remains limited in scope. Works such Jiang et al. (2024b) and Wen al. (2024) evaluated LLMs' cnttructionfollowwiin performance and explored the use of LLMs themselves for evaluation. However, their datasets were manually constructed, lacked training sets, and thus fell short in terms of scalability for large-scale applications. While AutolF (Dong et al. 2025) attempted to address data construction through an automated pipeline, relied exclusively on code- vnde-veriñablee constraints, which limits applicability to narrow domains and fails to capture broader, real-world use cases. More critically, most existing datasets impose only a small number of constraints (typically 3-5) and lack systematic evaluation tasks. Previous automated approaches
benchmarks or training resources for complex instruction-followwe primarily rely on LLM--bssed instruction rewriting, which often yields homogenized constraints and limits scalability in generating large numbers of constraints. Consequently, they are inadequate for rigorously assessing and extending the capability boundaries of LLMS in realistic, complex application settings.
To address these limitations and advance research in this area, we propose RECAST, scalable data- synthesis framework that constructs instruction-followiing datasets of unprecedented complexity. Unlike prior approaches, our pipeline systematically mines essrucctoo-followiing signals from existing data, thereby
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improving data utilization and fully exploiting the training value of available resources. Through the integration of diverse, verifiable constraints, RECAST enables more rigorous evaluation and more effective enhancement of LLMs' ability follow complex instructions.
Based on RECAST, we constructed a high-quality. diverse, and constraint-verrfibble dataset, RECAST- 30K, which encompasses large number of constraints spanning diverse types. Using only 30K training samples, Ilama3.1 -8B-Base (Grattafiori et al.) 2024) fine-tuned on RECAST-30K already outperforms other instruction-tuning datasets. Remarkably, surpasses the corresponding instruct model-traines on a much larger-scale and even outperforms the substantially larger -lama-3.3-70B-Imm. Instructt in handling complex instruction-followinng scenarios.
Given that the constraints in RECAST-30K can be reliably verified using both rule-based and model-based validators, the dataset inherently amenable to reinforcement learning. Building on this property, we design RLVC, which further enhances constraint compliance, yielding substantial improvements in multi-constraint satisfaction while preserving general capabilities. To summarize the highlights our study:
We propose RECAST, an efficient, scalable, and low-cost data-synthesis framework that constructs complex instruction-folllwing datasets of unprecedented complexity, aiming challenge and enhance state-of-the-ar models' ability to follow complex instructions.
We release RECAST-30K, high-quality dataset purpose-built to benchmark and improve complex mnstruction-followimn performance.
We further exploit the verifiable constraints in RECAST-30K to design RLVC, a reinforcement- learning approach that leverages constraint-specific reward signals for simultaneous multi-objective optimisation, thereby fully exploiting the supervision potential of the dataset.
2 REALISTIC EXTRACTION OF CONSTRAINTS FOR AUGMENTED INSTRUCTION SYNTHESIS
We present RECAST, comprehensive framework for improving LLMs' ability to follow complex instructions. As shown in Figure 2 the RECAST pipeline operates through three-stage process. First, construct rich constraint pool by extracting both rule-based and model-based constraints from seed data. Next, we synthesize enhanced instructions by selecting and naturally integrating appropriate constraints into original prompts. Finally, we generate consistent, constraint-complan responses to these augmented instructions.
2.1 SEED DATA COLLECTION
We select Tülu 3 Persona IF (Lambert et al. 2024) as the seed dataset for constructing RECAST-30K. This dataset spans diverse scenarios and task types, including code generation, creative writing, factual question answering, etc., rendering it appropriate foundation for constraint extraction. A more detailed analysis of the characteristics this seed dataset provided Appendix B.
2.2 CONSTRAINT POOL CONSTRUCTION
To effectively model the multifaceted nature of real-world instructions, we develop a comprehensive con- straint pool that captures both objective and subjective requirements. We categorize constraints into two complementary types based on their verification mechanisms, enabling precise assessment of model outputs against diverse criteria.
Rule-based Constraints Construction. Rule-based constraints represent objective requirements that can be verified through deterministic methods, including structural elements (paragraph count), lexical specifications
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Published as a conference paper ICLR 2026
RECAST: Realistic Extraction of Constraints for Augmented inStruction synThesis
RECAST-SFT
Seed Data Collection
Instructionn Enharscomeni
Response Synihesis
RECAST-3IK
Qwen2.5 .7B -BB-Ba
Dafa
Enihanced Instr
Enhanced
Instruction Response
Enharized Resp.
SFT
RECAST -30K-SFT
Consrratt Fool
Fedia
Find Constraints Which Previously didn't
Constraints Pool
Icer
hewd
RLVC: RL with Verifiable Constraints as rewards
[I] Contrrintt Matchin
Comtrratt Combicattion
Length
Instraction
21 Rule based Constraints Construcllon
Cantuar Eype
Case
Furmat
Appl slwey sat
seplex
Lengh
Outck lf Diopereet
Policy Model
Herk
Fermal
Formal
poree moots Basl
E390
Check the Sarposes
Ref Modal
Response
Average Reward
(1) Applicable Type identilication
Generation
(3) Fltering
Tonz
2E Model-based Constraints Construction
Comeuant Type
Teme
Purr Suyert
Group
Tomr
Eimotion
Computi
Eicuullry
Emuntion
Exrotion
Wheceer Teroon
Figure 2: Overview of the RECAST framework and RLVC. Left: The RECAST pipeline generates complex instruction-following data through four steps: (1) seed data collection across diverse domains, (2) constraint construction with both rule-based and model-based verification methods, (3) instruction enhancement by integrating selected constraints, and (4) response synthesis ensuring constraints are satisfied. More detailed descriptions the pipeline are provided in Appendix[A] Top-right: Using RECAST-generatte data fine-tune LLMs through SFT. Bottom-right: RLVC framework leveraging cunstrant-sseciife verification to provide fine-grained rewards, guiding model optimization toward satisfying multiple constraints simultaneously.
(keyword inclusion/exclusion), and quantitative parameters (word limits). These constraints are particularly valuable as they provide unambiguous verification signals for model training and evaluation. To systematically extract these constraints, we implement nine specialized rule-based extractors (detailed in Appendix [E.T) that analyze responses to identify verifiable properties. These extractors recognize specific syntactic patterns, keyword frequencies, numerical parameters, and structural characteristics that can be programmatically verified. This programmatic approach ensures that each rule-based constraint has a corresponding verification method, providing clear training signals for models to learn specific response characteristics.
Model-based Constraints Construction. Model-based constraints encompass subjective requirements that necessitate semantic understanding or qualitative judgment, such as stylistic elements (formality level), tonal qualities (politeness), and content characteristics (persuasiveness). These constraints are crucial for capturing the nuanced aspects of human communication that cannot be reduced to simple rules. Our construction process follows three key steps: First, we analyze each response from the seed datase to determine which constraint types from our taxonomy of 10 categories (detailed in Appendix E.2) are applicable, ensuring relevance to the specific content. Second, we employ LLMs to generate multiple concrete constraint instances for each applicable type, creating a comprehensive initial pool of model-based constraints. Third, we implement a filtering process to verify that each constraint is actually satisfied by its corresponding response, removing those that cannot be fulfilled. To validate the accuracy of filtering process, conducted human evaluation on randomly selected constraint-response pairs, confirming that our methodology successfully identifies and retains only appropriate, verifiable constraints (detailed results in Appendix C.T).
Through this dual-mode construction approach, we create a comprehensive constraint pool that captures both the objective, rule- verifiable aspects and the subjective, semantically-rich dimensions of instruction following.
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2.3 CONSTRAINT-AUGMENNT INSTRUCTION SYNTHESIS
After establishing verified constraint pool, we develop a three-stage process to create enhanced instructions that effectively incorporate appropriate constraints. This process aims to ensure that the resulting instructions maintain natural language quality while systematically integrating multiple constraints.
Constraint Selection. We employ LLMs to select coherent subset of constraints from the pool that are relevant and applicable to each original instruction. This selection process ensures that only contextually appropriate constraints are considered for integration, avoiding the inclusion of irrelevant contradictory requirements.
Instruction Enhancement. Once appropriate constraints identified, task multiple LLMs with integrating the selected constraints into the original instruction. Each model produces an enhanced instruction where constraints incorporated naturally and coherently into the text.
Optimal Instruction Selection. To ensure quality and consistency, we implement majority voting mecha- nism wherein multiple LLMs evaluate and rank the candidate integrated instructions. The evaluation criteria include linguistic fluency, semantic coherence, and constraint completeness. We select the highest-ranked instruction the final constraint-augmennt instruction. We also performed random sampling on optimal instruction selection for human evaluation, the results of human evaluation are shown in Appendix C.2
2.4 ESSTUCTION-CONNISTERR RESPONSE SYNTHESIS
To address potential inconsistencies between menstraint-augmentee instructions and original responses the seed dataset, we implement two-phase process ensure alignment while maintaining high response quality.
Diverse Response Generation. We generate multiple candidate responses to each constraint-augmented instruction using distinct LLMs. This multi-model approach introduces diversity into the candidate pool and mitigates potential biases inherent to any single model. Each model generates responses independently, ensuring a broad representation of possible approaches to satisfying the complex instruction.
Response Quality Assessment. We adopt majority voting scheme considering constraint adherence, accuracy, conciseness, etc., to select the best response for each instruction. The chosen response is added the dataset with its enhanced instruction. Human evaluation on sampled pairs (Appendix C.3) confirms the high quality of the selected responses.
This methodical approach to generation and selection ensures that our final dataset comprises constraint-rich and high-quality instruction-responsee pairs that demonstrate effective navigation of complex, multi-constraint scenarios. Each pair is annotated with its full set of constraints and corresponding validation method, facilitating downstream application and evaluation.
3 REINFORCEMENT LEARNING WITH VERIFIABLE CONSTRAINTS AS REWARDS
Since the constraints RECAST are equipped with corresponding verification methods, the dataset inherently amenable to reinforcement learning. We therefore introduce RLVC, which leverages the verifiable nature of constraints to provide fine-grained reward signals during policy optimization. Building on SFT- trained models, further apply RL maximize the training value of RECAST-30K for enhancing complex capabilities. This approach enables more targeted feedback on constraint satisfaction, thereby improving models' ability simultaneously address multiple complex requirements.
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3.1 CONSTRAINT VERIFICATION MECHANISMS
We implement a dual-mode verification scheme that enables precise evaluation of constraint satisfaction. Each constraint an instruction is assessed through either rule-based or LLM-based verification methods, For rule-based constraints, employ rule-based validators Vuule-bwed (2, y, c;) that programmatically verify objective requirements through deterministic procedures. For model- based constraints that require evaluation of subjective qualities, leverage LLM-based validators Viedee-hhsed (2,, ys c:). These validators assess more nuanced requirements that resist codification into explicit codes. We formalize the constraint verification function for an instruction with response y and constraint O2 as:
brule-bused (I, v.) Vmodes bused (2, M. ca)
if c2 is rule-based
fa,
(1)
if c, is model-based
where both verification functions return binary values indicating constraint satisfaction (1) or violation (0).
3.2 VERIFIABLE CONSTRAINTS AS REWARD SIGNALS
The key innovation of RLVC lies in its exploitation of RECAST's constraint verifiability feature. Traditional reinforcement learning approaches typically rely on a single, holistic reward signal that fails identify which specific constraints are violated. In contrast, RECAST-30K provides individually verifiable constraints, enabling us to design a more informative reward mechanism. For an instruction r containing multiple constraints C' = {ei, Cn, we define the reward for generated response as the average satisfaction rate across all constraints:
1 |C| Σ f(x,z,c)
R(E, u)
(2)
This design provides independent reward channel for each constraint, offering fine- fine-grained feedback that guides the model toward simultancously satisfying multiple constraints. Our RLVC framework combines verifiable constraints as reward signals with Group Relative Policy Optimization (GRPO) (Shao et al. 2024). Specifically, RLVC leverages the fine-grained feedback enabled by constraint verifiability and optimizes policies via GRPO's 's comparative learning approach. This integration provides the model with clear guidance improving constraint satisfaction across diverse instruction types. For completeness. we present detailed formulation of GRPO, including optimization objective, Appendix D.5
4
EXPERIMENTS
4.1
SETUPS
Evaluation Benchmarks. We constructed RECAST-Test, a hierarchical benchmark with four difficulty levels defined by increasing constraint complexity, enabling fine-grained evaluation across tasks of varying difficulty. Crucially, RECAST- constraints than prior benchmarks, providing more rigorous assessment of LLMs' Test contains substantially capabilities. Details of its construction and statistics are Appendix B.4 To further assess complex instruction forllooinoooowinggggooooooon generalization, we also evaluate RECAST on four external benchmarks (Appendix (D.2))
Evaluation Metrics. For evaluation metrics, we use the Hard Constraint Satisfaction Rate (HSR) as the primary metric, quantifying the model's 's ability to satisfy all specified constraints in an instruction simultaneously. The HSR is calculated follows:
HSR
Σ
II f(2,v,c)
(3)
D
(..g)E CEC(()
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Table I: RECAST-Test results across four difficulty levels and overall average. RSR, MSR, and OSR denote the rule-based, model-based, and overall components of the holistic success rate (HSR), respectively. The best result each column is highlighted in bold, while the second-highest result indicated with underline formatting. All reported values are percentages, with the percent sign omitted for brevity.
Models
MSR
Level RSR
OSR
Level2
RSR
OSR
Level
RSR
OSR
MSR
Level
OSR
Average
Claude-3,7 3andd-3.7-sonneet
90.00
15.50
13.50
86.50
10.00
7.00
76.00)
5.50
5,001
613.00
6.50
4.30
31.67
DeepSeekc-V3
86.50
11.50
9.00
64.00
5.00
Ge5ini-2.5-PPro
91.50
25.50
22 (10
87.50
21.00
19.00
79.00
16.50
13.00
68.30
20,00
13.50
39.75
GPT-4o
89.50
21.50
22. 22.50
85.00
15.00
10.50
T4.50
7.50
62.50
7,00
31.46
Qwen33228BBAA22
96.00
25.50
25.00
$7.00
13.50
10.00
83.00
12.00
10.00
71.00
7:00
5.50
27_12
88.00
23.50
20.50
79.50
16.50
12.00
70.00
8.50
6.50
64.50
6.50
4.50
33.35
Owenn.5-7B--nsste
83.50
21.50
18.50
69.00
12.30
7.50
51.00
3.30
2.00
40..0
5.00)
3.00
26..46
Llama-3,3- 3,3-70B- Instructt
79.00
25.00
20.00
56.00
14.50
9:00
13.00
9.00
3.00
30:00
6.00
2.50
21.75
Llama-3.1-80 3.------eettt
86.50
18.50
15.50
73.00
12.50
8.00
63.50
8.50
37.00
3.50
27.26
I.lama-3.1-88
Coniter
73.00
15,00
11.50
540.003
10.00
35.00
45.00
4.50
2.50
32.50
01.50)
(1.51)
21,000
Crab
6.00
33.50
3.00
3.00
12.00
0.50
I-SHEEP
66.00
14.00
8.75
43.77
9.00
3.00
34.75
3.75
1.50
22.25
1.50
0.50
17.40
MUFFIN
34.50
6.00
1.50
19.00
4.50
2.00
10.00
1.00
13.50
1.50
0.00
7.79
ShareGPT
71.00
16.00
10.50
49.00
5.00
2,00
38..00
3.00
0.50
27.30
2.50
1.000
18.88
Evol-Instruct
62.50
17.00
10.50
12.50
6.50
3.00
28. 28.00
3.50
0.50
20.50
2.50
1.00
16.50
Suri
3.00
3.00
0.00
1.00
0.00
0.00
0.50
0.00
Tiilu 3 Persona IF
83.00
22.00
19.50
70.50
9.00
5.50
55.50
8.00
2.50
42.00
4,00
2.00
26.96
RECAST-30K
21.00
18.50
78.50
9.50
63..0
6.00
52.00
8..00
4.50
300.54
RECAST.3K-RRAD
83.50
21.00
17.00
79.50
17.50
12.50
64,00
11.50
7.50
53.00
10.00
6.50
31.96
Qwen-225-718
Conieer
71.001
16.00
14.00
58.50
9.00
5.50
45.50
6.00
3.50
33.50
3.00
1.000
22.21
Crab
57.50
14.00
7.50
330.0)
7.00
33.00
201.0
5.00
1.00
15.30
5.50
1.50
14.000
I-SHEEP
56.00
12.50
7.50
35.50
6.00
1.00
26.00
3.50
0.00
18.00
1.50
0.00
13.99
MUFFIN
61.50
17:00
10.00
46.00
7.00
4.50
25.50
3.50
1.00
25.00
1.00
17.13
ShareGPT
70.50
13.50
11.00
46.50
9.50
4.00
34.50
3.50
2.50
25.00
4.50
1.50
19.13
Evol-Instruct
63.50
14.50
8.00
41.00
3.00
27.00
2.50
1.00
22.50
4.50
2.00
16.46
Suri
0.50
4.50
0.00
0.50
2.00
0.00
0.00
0.50
0.00
0.00
0.00
0.00
0.67
69.00
7.00
4.50
45.00
1.00
RECAST-30K-SFF
87.50
18.58 50
11.50
76.30
17.00
13.00
66.50
11.50
6.50
5/1.00
5,01)
4.50
31.25
RECAST-3OK-RLVV
85.50
16.50
78.00
12.50
10.50
55.50
7.00
32.33
where D represents evaluation dataset, ((x) the set of constraints instruction æ, and (x, y, es) is the verification function for constraint C,.
Additionally, we define the following sub-metries for different constraint types: (1) Rule-based Constraint Satisfaction Rate (RSR), which measures the HSR score for constraints evaluated using rule-based methods; (2) Model-hased Constraint Satisfaction Rate (MSR), which measures the HSR score for constraints evaluated using L.IMs;; and (3) Overall HSR constraints, both rule-based and model-based. that are successfully satisfied simultaneously. For the FolcowBench evaluation, we similarly adopt the Hard Constraint Satisfaction Rate defined in the original benchmark. For all LLM-based evaluations, unless otherwise specified, we default to using GPT-4o (Achiam et al. 2023) for assessment.
Constraint Satisfaction Rate (OSR), which quantifies
Baselines. For our baseline comparisons, we primarily select eight high-quality, open-source complex instruction fine-tuning datasets. A detailed description of baselines can be found Appendix [D.]] Additionally, we evaluated the performance of some of the leading large models on the benchmark we constructed.
Settings. For all experiments, we choose Qwen2.5-7B Yang et al. 2024 and Llama-3.1 1-B (Grattafiori et al. 2024 as the base models. The same experimental sctup is used for SFT across all datascts. We use the model after SFT the base model of RLVC for training. Specific experimental details be found in Appendix
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4.2
MAIN RESULTS
4.2.1
EVALUATION ON RECAST-TEST
Table D presents comprehensive results from multi-level constraint satisfaction benchmark. Analysis of these findings reveals three results:
Performance Degradation with Increasing Constraint Complexity. Across all models, performance consistently declines from Level to Level 4, underscoring the difficulty of satisfying multiple constraints simultaneously. Rule-based constraints prove more challenging than model-based ones. Gemini-2.5-PP 2.5-Pro shows the best performance with 39.75% average satisfaction rate. Overall, complex instruction following remains difficult for current LLMs, highlighting the need for further advancement.
Effectiveness of RECAST-30K for Instruction Following. Models fine-tuned on RECAST-30K demonstrate substantial improvements in constraint satisfaction capabilities. The RECAST-30K-SD variant achieves a 31.25% average satisfaction rate on Qwen-2.5-7B, significantly outperforming both instruction-tumed models and alternative fine-tuning approaches. This enhancement underscores the effectiveness of cnoruction-follwwiin capabilities, particularly for navigating complex, multi-consteen constrait scenarios.
focusee dataset in developing robust
Performance Enhancement Through RLVC Optimization. The application of RLVC further enhances model perfor- mance across all difficulty levels. RECAST-306 S---3K---LVCC variants consistently outperform their SFT counterparts, achieving 32.33% average satisfaction rate Dwen-2.5-7B. This improvement particularly pronounced in higher difficulty levels (Levels 3-4), where constraint interactions become more complex. These gains confirm that our constraint-specific reinforcement learning approach effectively optimizes for simultaneous satisfaction of multiple requirements, addressing a key challenge in practical complex instruction following.
These results clearly demonstrate that specialized fine-tuning approaches-parriculane our RECAST-30K dataset and RLVC optimization simultancously. This improvement addresses critical gap in current instruction-followin systems and enables reliable performance in constraint-rich applications.
signiffcaaiy enhance I.L.s' capability to satisfy multiple complex constraints
Comparison with Divide- Verify-Refine (DVR). We further compare our models with the inference-ting instruction- following enhancement method Divide- Divide-Verify-Renn Verify-Refine (DVR) (Zhang et al. 2025) on RECAST-Tes. Since DVR in its original form only leverages mule-baedd verifiers, we report the Rulc-based Constraint Satisfaction Rate (RSR) for fair comparison. As shown in Table 2 Instruct leads to substantially lower RSR than our RECAST-30K-SD models, which consistently achieve higher RSR across all difficulty levels.
applying DVR at inference time on top of Llama-3. 3.1--...BBBBBBBB 1-8B--Instrun and Qwen2.5-7B.
Table 2: RSR results of Divide Seale-Verrfy-Refiiee (DVR) compared with RECAST-30K-SE models on RECAST-Test. We report Rule-based Constraint Satisfaction Rate (RSR) across four difficulty levels and their average. The best result in each column is highlighted in bold, while the second-highest result is indicated with underline formatting. All reported values are percentages, with the percent sign omitted for brevity.
Model
Level 1
Level
Level 3
Level 4
Avg.
1lama-3.1-8B-Instttee + DVR
14.0
9.5
2.0
2.5
7.0
Qwwen2.5-7B-Innsrruct + DVR
11.0
8.5
4.5
2.0
6.5
RECAST-3OK-SPP (Ours, Llama base)
21.0
15.0
6.0
8.0
12.5
RECAST:30K-SFT (Ours, Qwen base)
18.5
17.0
11.5
5.0
13.0
These results show that, for both Llama-3. and Qwen2.5-7B, RECAST-30K-95 achieves substantially higher average RSR than their Dnhaenndd counterparts: 12.5% vs. 7.0% on Llama (+5.5% points) and 13.0% vs. 6.5% on Qwen (+6.5% points). The gains are consistent across all difficulty levels (Levels 1-4), typically ranging from 3% to 8% absolute points. This suggests that training-timn time RECAST-30K pipeline provides complementary benefits beyond inference-time self-alignment, leading more robust multi-constraint satisfaction.
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Table 3: Results on instruction- ciiructiio-foolowinng benchmarks (IFEVAL and FollowBench), IFEVAL uses Prompt- Level Loose Accuracy (Pr: (L)), while FollowBench uses Hard Satisfaction Rate (HSR). The best result in each column is highlighted in bold, while the second-highest result is indicated with underline formatting. All reported values percentages, with the percent sign omitted for brevity.
LIama3.1
QWEN2.5-7B
Models
IFEVAL
Followbench
Avg.
IFEVAL
Followbench
Avg.
(I.)
HSR
Pv. (TJ
HSR
Conifer
43.62
44.16
44.73
52.27
48.50
Crab
42.14
41.90
42.02
44.55
44.50
44.53
I-SHEEP
32.90
29.48
31.19
36.60
35.38
35.99
MUFFIN
24.95
16.78
20.87
36.78
42.15
39.47
ShareGPT
46.40
45.41
45.91
47.13
49.06
48.10
Evol-Instruct
42.70
39.65
45.29
46.34
45.82
Suri
18.30
4.02
11.16
12.75
5.59
9.17
Tülu 3 Persona IF
73.94
55.44
641.99
73.94
56.94
65.44
RECAST-30K-SPT
76.34
57.10
66.72
73.38
59.60
66.49
RECAST-30K-RIYC
77.39
61.76
69.58
74.01
63.23
68.62
Table 4: Results general capability benchmarks (GPQA and MUSR). All metries standard Accuracy (Acc), measuring the percentage of correct responses. The best result in each column is highlighted in bold, while the second-highest result indicated with underline formatting. All reported values are percentages, with the percent sign omitted for brevity.
Llama3.1-8B
QWEN2.5-7B
Models
GPQA
MUSR
Avg.
GPQA
MUSR
Avg.
Ace
Acc
Conifer
27.61
37.64
32.63
31.22
45.54
38.38
Crab
32.48
10.26
36.37
31.89
48.74
40.32
I-SHEEP
28.33
36.30
32.32
29.95
43.18
36.57
MUFFIN
26.64
33.69
30.17
29.07
37.93
33.50
ShareGPT
30.02
40.16
35.09
31.20
44.95
38.08
Evol-Instruct
28.41
41.75
35.08
32.55
47.41
39.98
Suri
26.09
33.40
29.75
26.60
41.79
34.20
Tülu 3 Persona IF
28.64
38.68
33.66
31.06
44.13
37.60
RECAST-30K-SF7
26.77
37.39
32.08
30.02
39.91
34.97
RECAST-30K-
30.81
38.47
34.64
29.84
46.94
38.39
4.2.2 EVALUATION ON ADDITIONAL INSTRUCTION-FOLLOWINGG BENCHMARKS.
Table reports results on two widely used instruction-followee benchmarks. IFEVAL and FollowBencch RECAST- 30K-S1 SFT already surpasses all baseline methods, and RECAST-30K RLVC further improves performance, achicving the best results on both Llama3.1-3 1-8B and QWEN2.5-7B. These consistent gains on out-of off-of---miin benchmarks demonstrate that RECAST not only improves complex constraint-followin following but also generalizes effectively traditional instruction- following tasks, rather than merely overfitting to the constraint patterns during training.

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Published as a conference paper ICLR 2026
4.2.3 GENERAL CAPABILITY EVALUATION
addition, we evaluate RECAST on two general capability benchmarks (Table where RECAST-30K-RLV achieves average scores of 31.64% on Llama3. and 38.39% on QWEN2.5-7B. This indicates that while RECAST substantially improves performance on complex instruction following. also preserves competitive performance on reasoning-oriented benchmarks like MUSR and wwwwleege--ccuseee evaluations such as GPQA.
5 RELATED WORK
Complex Instruction Datasets Construction. Recent work has proposed various methods for constructing complex instruction datasets. Xu et al. (2024) proposed Evol-Instruct, which increases difficulty through iterative rewriting, (Sun) al. (2024) introduced Conifer. which generates multi-level constraints with GPT-4, [An et al. (2025) proposed ULTRAIF,
which decomposes and recombines instructions, and Liu et al. (2025) introduced AIR, which adds constraints via a model-judge cycle. Other approaches include MUFFIN by| Lou al. (2023), which expands input features, and CRaB by
Qi et (2024), which infers constraints via baak-transllttinn. In contrast, RECAST mines diverse constraint types directly from seed responses and integrates explicit verification for both rule-based and model-based constraints, addressing limitations in constraint complexity, diversity, and quality assurance.
Complex Instruction Following Capabilities Improvement. Recent algorithmic innovations have significantly advanced instruction following capabillites. Dong ct al. (2025) proposed AutolF, which implements self-dialogue generation process with exccution-based verification, filtering training data through executable feedback. |aag et al. (2024a) introduced RNR, which extracts roles and rules from existing instructions to generate rule-compliant responses, while Cheng et al. (2025) proposed SPaR, which employs self-play tree search refinement through multiple rounds of model self- adversarial interaction optimize response quality. The approach in (He al. 2024) utilizes discriminative generation, applying a discriminator model to filter generated samples for higher-quality supervision. Unlike prior methods, we leverage RECAST-30K's quantitative and qualitative constraints, using multi multi-channel rewards in RLVC to treat each constraint as separate optimization target, significantly improving accuracy and consistency in complex instruction following.
Reinforcement Learning with Verifiable Rewards. A closely related line of work studies reinforcement learning with verifiable rewards (RLVR) for LLMs (Lambert et al.) [024] [Su et al. 2025 Waan et al. 2025 Pyatkin et al. 2025). These methods replace or complement scalar-valued reward models with rewards obtained from rule-based or model- based verifiers, and have demonstrated strong gains in domains where answer correctness can be automatically checked. Lambert et al. (2024) and Wang al. (2025) apply verifiable rewards to math, code, and other tasks with deterministic checkers, while Su al. (2025) extends RLVR to broader domains and shows that verifiable rewards can serve a bridge between different task familics. PPytkin et al. (2025) further investigates how verifiable reward mechanisms generalize to multi-constraint and cormat-constrained instruction following. Most of these works, however, still assume a single reward channel, either purely rule-based or purely model-based. By contrast, RLVC formulates rewards at the per-constraint level and explicitly combines rule-based and model-based verifiers within unified framework, yielding richer partial-credit signals for complex instruction-fol forluciion-oolooiingg tasks.
6 CONCLUSION
We present RECAST, a scalable, low-cost framework for constructing complex instruction-followiing datasets through systematic mining and verification of rule- and model-based constraints. Using this, we release RECAST-30K, enabling smaller models (e.g.. Llama3.1-880 propose RLVC, reinforcement learning method using fine-grained, constraint-specifie rewards to enhance performance. Experiments show RECAST-traine, models excel on our hicrarchical benchmark, generalize to IFEVAL and WollowBench, and remain competitive on general tasks (GPQA, MUSR). RECAST advances automated data construction by offering scalable, verifiable supervision, improving constraint diversity, complexity, and quality, and providing practical resources for more controllable LLMs,, and advancing their real-world applications.
outperform larger instruct-tuned models. Leveraging constraint verifiability, we
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ETHICS STATEMENT
This work focuses on constructing complex instruction following dataset and training methods for large language models (LLMs). All data used in RECAST-30K is derived from publicly available. non-sensitive sources and does not involve private personally identifiable information. To mitigate risks of bias and harmful outputs, we systematically design rule-based and model-basee constraints with explicit verification mechanisms, ensuring high-quality and safe training signals. While our framework substantially improves models' ability to follow complex, multi-constrain instructions, we recognize that enhanced controllability can also be misused (e.g., for generating manipulative or deceptive content). We therefore emphasize that RECAST and RLVC are intended for advancing the reliability and transparency of LLMs in research and socially beneficial applications. We encourage responsible and further auditing to address potential fairness, bias, and misuse concern..
REPRODUCIBILITY STATEMENT
We provide detailed descriptions to facilitate reproducibility of our work. In Section 2 we introduce the design and
methodology of RECAST. with further details of the data construction pipeline presented in Appendix [A] Additional implementation specifications, including API usage for data generation. are described in Appendix [.3] The supervised fine-tuning (SFT) setup, including hyperparameters and engineering resources, is documented in Appendix [D.4] For reinforcement learning with verifiable constraints (RLVC), we describe the reward function design in Section ] and
provide theoretical formulations, implementation resources, and training dynamics in Appendix D.5 If accepted, we will release the code, data, and trained models on Gu to further support reproducibility.
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Yiping Wang, Qing Yang, Zhiyuan Zeng, Liliang Ren, Liyuan Liu, Baolin Peng, Hao Cheng, Xuehai He, Kuan Wang. Jianfeng Gao, et al. Reinforcement learning for reasoning in large language models with one training example. arXiv preprint arXiv:2504.20577 2025.
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RECAST: Realistic Extraction of Constraints for Augmented inStruction syn Thesis
SSed Data Collection
Instruction Enhancement
+ Response Synthesis
Enhanced Instr.
Data
Enhanced Instr.
Enhanced Resp,
Instruction
Constraints Pool
Response
Constraints Pool
Emolion
Format Leugth
Constraints Which Previously didn't 't Exist instruction
lmotion
Tume
Fermat
Lenuth
(1) Constraint Matching
Constraint Combination
Rule-based Constraints Construction
Constraint Type
Langth Format Mark Cas
Case
Format
Apply lomplate
Apply template
Length
200 words macimen
Length
Apply template
Format
Rasgund in paoe format
Whether can constrained?
Case
No
Type
Dinepani
Response
Applicable Constraint Type Identification
(2) Constraint Generation
(3) Constraint Filtering
Model-based Constraints
Constraint Type
Emuttion Factuality Length
Yone
Tone Emotion
Tone
Be passionate
lively
Emotion You
No
Construction
Responce can be constrained by...
Inter the most lieey constrannts..
Response You adheres to the constralic?
Overview of the RECAST framework. The RECAST pipeline generates complex instruction- following data through four steps: (1) seed data collection across diverse domains, (2) constraint construction with both rule-based and model-based verification methods, (3) instruction enhancement by integrating selected constraints, and (4) response synthesis ensuring constraints are satisfied.
Figure 3:
A DETAILED DESCRIPTION OF THE RECAST DATA CONSTRUCTION PIPELINE
To facilitate future research in complex instruction following and to support reproducibility. we provide a comprehensive description of the RECAST data construction pipeline in this appendix. The following exposition is presented in conjunction with Figure 3 which illustrates the pipeline workflow and should help readers hetter understand the individual steps. We believe this detailed methodology will serve as valuable reference for future work on dataset construction evaluation.
The pipeline begins with the Tülu 3 Persona IF dataset (Lambert et al. 2024) as the seed dataset, which contains instruction-response pairs. Based these responses, construct both rule-hased and model-based constraints, which be processed parallel.
For rule-based constraints, we define multiple types (see Table [1] each associated with validation function (rule-based validator). These functions determine whether response satisfies the conditions for generating a rule-based constraint. When condition is met, a matching template from our constraint template set (Appendix E.3) is selected to generate the corresponding constraint. For example, if a response is validated as JSON, may select the template "Format your answer valid and instantiate it with "json" form "Format your answer valid 'json" "M (Step 2.1 in Figure 2
model-based constraints, define multiple types (see Table 12). An LLM first analyzes each response to identify applicable constraint types (Applicable Constraint Type Identification, Step 2.2 in Figure 2 Another prompt is then issued to generate specific constraints for each response (Step 2.2 in Figure 2 see prompt in Figure 22). Since some generated constraints may be spurious, we apply constraint filtering retain only those actually satisfied by the response (Step 2.2 in Figure
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After constructing both cule-based and model- constraints, we a constraint pool for each response. An LLM then compares the original instruction with the constraint pool to identffy missing constraints. These are incorporated into the instruction through rewriting. producing an Enhanced Instruction. To ensure rewrite quality, multiple LLMs are queried to generate candidate rewrites, followed by majority voting (Table S to select the best version.
Finally, given the enhanced instructions, response diversity while ensuring alignment the enhanced instructions. Majority voting again applied to select the best candidate, yielding an Enhanced Response. The resulting RECAST-30K dataset thus consists of Enhanced Instruction Enhrinee-- Response pairs, systematically constructed through this pipeline.
multiple LLMs to generate responses. This procedure increases
DATASET ANALYSIS
In this section, we first introduce the characteristics of the seed dataset Tülu 3 Persona IF (Lambert et al. 2024) to justify its selection as the basis for our work, and then provide detailed analysis of RECAST, the dataset constructed for improving complex instruction following, with particular focus on the characteristies of the constraints designed and their distribution across different types.
B. SEED DATASET ANALYSIS
The seed dataset for RECAST-30K Tulu 3 Persona IF, subset of Tülu containing 30K prompt-response pairs. We chose this dataset as our starting point because spans wide range domains and includes responses rich content-making it especially well-suited for constructing diverse and realistic constraints in our RECAST pipeline.
To further illustrate the breadth of task coverage in our seed dataset, we provide below breakdown of the domain distribution. This diversity key factor that enables framework to generate wide variety of instruction instruction-following tasks with meaningful, verifiable constraints. This domain-level diversity ensures that RECAST-30K dataset supports constraint generation across a broad spectrum of real-world scenarios.
Our pipeline method is not limited to the currently used seed dataset; it is equally applicable to other corpora containing rich information. We chose this dataset primarily for ease of engineering implementation.
B.2 RECAST-30K
To rigorously evaluate the ability of language models to follow complex, constraint-rich instructions, we construct a dataset consisting of 29,939 examples, which refer to as RECAST-30K. RECAST-30K stands out for its high density of constraints, setting apart from existing instruction datasets.
All Constraints
Rule-Based
Model-Based
10003
10000
600)
4003
2803
50
2
Nambbar Constraints
Figure 4: Constraint Count Distribution of RECAST-30K.
B.2.1 CONSTRAINT DENSITY
Each instruction in RECAST-30K is enriched with average of 13.4 constraints drawn from different types. Among these, average of 8.6 constraints generated via large language models (model-based constraints), while 4.8 constraints per
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Table 5: Domain distribution of the Tülu 3 Persona IF seed dataset.
Domain
Number of Instances
Storytelling
2334
Social Media Post
740
Advertising Copy
1 1196
Scriptwriting & Dialogue
616
Poetry
278
Essay/Blog/Review
3390
Lyrics
93
Finance
643
Legal
612
Programming & Technology
1376
Medical & Health
917
Education
4220
Daily Life
981
Internet/IT
126
Data Analysis
997
Translation
147
Summarization
992
Information Retrieval
912
Science Popularization
1556
News Reporting
610
Entertainment
294
Business & Management
1296
Politics & International Affairs
672
Scientific Research
577
Psychology
143
Role Playing
233
Other
1819
instruction are generated using based heuristics (rule-based constraints), shows the constraint density (i.e., constraints per instruction) distribution of RECAST-30K. The results suggest that RECAST-30K achieves high level of constraint density, underscoring the dataset's 's broad coverage constraints. This further evidenced by the following statistics:
36.4% % (10,884 samples) instructions contain or more constraints:
78.8% % (23,580 samples) of instructions contain 10 or more constraints;
'The instruction with the highest complexity contains over 30 constraints.
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Rule-Based
Model- eaaeed
1000
Count
0000
1000
2002
2000
0
Figure 5: Constraint Count Distribution of Train Set with Constraints.
Rule-Based
Model-Bases
0030
3000
COUrt
4000
3030
2000
2000
Number of Constraints
Figure 6: Constraint Count Distribution of Train Set with 10 Constraints.
As discussed in Appendix D.6 we also provide detailed statistics under different setups Figure:5 and showing distribution of datasets' variants with different maximum constraint limits (5, 10, and 15).
This level of constraint density pushes the limits of current models' ability to handle compositional and multi-facete constraints, representing significant lcap beyond prior instruction datasets.
B.2.2 CONSTRAINT TYPES
In this section, provide in-depth analysis the distribution of constraint type RECAST-30K and its variants.
To understand how constraint types are distrihuted in RECAST- 30K, visualize their distribution in Figure 8 The results suggest that RECAST-30K contains highly diverse set of constraints that closely reflect real-world user instructions. Similarly, provide results of the RECAST-30K variants with reduced sets constraints. Figures I0 and II visualize the constraint type distributions when the number constraints is limited to 5, 10, and 15, respectively.
Visualizing the Distribution of Constraint Types
Analysis of the Most and Least Common Constraint Types To provide further insights into the constraint type distribution in RECAST-30K, we examine which constraint types occur most least frequently The results are shown Table For model-based constraints, the three most frequently applied types are Style, Background Information, and Format, whereas the least frequent types are Emotion, Role Playing, and Situation. These model- based constraints span broad range of semantic and stylistic dimensions. For rule-based constraints, the most common types Length, Keyword, and Start With, while the least common types All Lowercase, All Uppercase, and No Commas.
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saleeBBased
Model Bal--Bae Based
4000
2000
Count
0003
2900
1900
1000
2000
5
8
10
Number Constraints
Figure 7: Constraint Count Distribution of Train Set with 15 Constraints.
Baal--Bood
Kaym tsaarss
Lmgll Are Komeu crss
Eing og sert
2te L330)
Mope Dooad
Kuln leva
Figure 8: Constraint Type Distribution RECAST-30K.
This complementary distribution reflects the design goal of balancing semantic richness (via model-based constraints) with the granularity of constraints (via rule-based constraints). The constraints effectively supplement the J.L--generate ones by covering fine grained often under under-representer aspects output control, thus improving both the coverage and accuracy of constraint specification.
B.2.3 DISCUSSION ON REALISM AND DIVERSITY
One of the key advantages of RECAST is that the constraints were not arbitrarily imposed but instead derived from naturally accurring responses. This ensures that the constraints are realistically achievable and semantically coherent with the task context. In addition to their authenticity, the constraints in RECAST-30K exhibit remarkable density, with many instructions containing large numher of constraints, and show high diversity across different constraint types. As result, RECAST-30K serves as rich and challenging training resource enhancing models' ability to follow complex instructions in settings that closely resemble real-world usage, where users often issue requests involving multiple, varied, and fine-grad tne-ggrined constraints.
B.3 CoST ANALYSIS CONSTRUCTING RECAST-30K
To provide transparency on the resource requirements of building our dataset, we report the estimated costs incurred at each stage of the RECAST pipeline. The total expenditure was approximately $175, broken down shown in Table 7
These figures demonstrate that RECAST-30K can be constructed relatively low cost, underscoring the scalability and practicality of our pipeline for broader adoption in future instruction-following research.
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Toaal
Leigh CSARD
RuleBaasd
Model Bared
Taper RTAD
Logll Duansi
Heget looe havi
Lang og AAR)
Comman 2011 Liveal OUNi
Somerted Coneemn SCTS)
Dranden 2710
Prundend 2013
Figure 9: Constraint Type Distribution of Train Set with 5 Constraints.
Table Most and Least Frequent Constraint Types
Construction Method
Most Frequent Type (Top 3)
Least Frequent Type (Top 3)
Emotion, Role Playing, Situation
Model-Based
Style, Background Information, Format
All Lowercase, All Uppercase. No Commas
Rulc-Based
Length, Keyword, Start With
B.4 RECAST-TESS
To facilitate rigorous evaluation of complex instruction following capabilities constructed RECAST-Test through structured, progressive constraint selection approach. We began by randomly sampling 500 data points from RECAST-30K that contained at least 15 distinct constraints per instruction, establishing a robust foundation of constraint- rich examples. This metadata served as the basis for our hierarchical benchmark construction.
varying difficulty levels, we
The key innovation in RECAST-Test lies its carefully calibrated difficulty progression. We structured the benchmark four distinct complexity by implementing a constraint mensraint-neetiing methodology:
Level 1 (Basic): Each instruction incorporates 5 constraints, providing baseline assessment of fundamental fnslluctoon-ollooinng capability.
Level 2 (Intermediate): Instructions contain 10 constraints, including all 5 constraints from Level 1 plus 5 additional requirements.
Level (Advanced); Instructions feature constraints, encompassing all constraints from Level with additional requirements.
Level 4 (Comprehensive): Instructions include all available constraints for each sample, representing the maximum complexity level.
This nested constraint design ensures consistent difficulty progression while maintaining semantic coherence across levels. By preserving all constraints from previous levels, we enable direct performance comparisons complexity increases.
For each difficulty tier, employed the instruction enhancement pipeline from RECAST seamlessly integrate the selected constraints into coherent prompts. This process involved using majority voting among multiple LLMs to generate linguistically natural instructions that incorporate all specified constraints without compromising readability or coherence. The resulting benchmark comprises four progressively challenging evaluation sets, each containing the same underlying tasks but with increasing constraint complexity. allowing for controlled assessment of instruction followiiiiooooowiiiggggoo capabilities under varying levels of constraint demands.
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Total
Leigh cuart burrts
RuleBasod
Model Bared
Tpper ETC
Lange (--tr)
NUTO
Cumman 2011 Liveal OUt0
Somerted Coneemn
Prundend Io13
Figure 10: Constraint Type Distribution of Train Set with 10 Constraints.
Total
Buk Baxoi
Modd Boca
Aepooed
and ans
Hele Jeve A.no
Kmah bocett
Remmalty (noig)
Errge (Lmne
the coang)
Kang
Niternod Conmeme
Conrre 3000
Lowwrt 201
Figure 11: Constraint Type Distribution of Train Set with 15 Constraints.
Visualizing the Distribution of Constraint Density provide comprehensive analysis constraint density distribution within the RECAST-Test, we conducted an in-depth examination of the constraint density across different subsets of RECAST-Tess Test. Figures 12 13 14 and 15 present the constraint density distributions of the full RECAST-Test and its variants with maximum constraints limit of 5. 10. 15, respectively. These results collectively demonstrate the richness of constraint the RECAST-TesL which crucial evaluating effectiveness of models' detailed analysis of constraint density distribution across different subsets
ability follow complex instructions. provides valuable insights into the dataset's 's structure and utility.
Constraints
BaleeBBased
Basse
10
100
Number Constmints
Figure 12: Constraint Density Distribution of RECAST-Tec Test with All Constraints
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Table 7: Estimated cost of constructing RECAST-30K across different pipeline stages.
Pipeline Stage
Estimated Cost (USD)
Rule-based Constraints Construction
$0 (no API calls required)
Model-based Constraints Construction
S70
Instruction Enhancement
S85
Majority Voting
S20
Total
$175
Rule-Based
Model-Based
150
100
Coint
100
Number of Constraints
Figure 13: Constraint Density Distribution of RECAST-Test with 5 Constraints.
Rule-Based
Model-Based
140
140
130
100
Count
10
Figure 14: Constraint Density Distribution of RECAST-Tes, with 10 Constraints.
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Rule-B Bale-Based
140
120
Count
90
40
40
10
11
Number Constraints
Figure 15: Constraint Density Distribution of RECAST-Test with 15 Constraints.
Visualizing the Distribution of Constraint Types Similarly, Figures T6 17
18 19 illustrates the constraint
type distributions of the full RECAST-Test with all constraints included and its variants. These results demonstrate that RECAST-Test covers highly diverse range of constraint types, effectively capturing the complexity and variability found real-world instructions. The detailed breakdown constraint type distributions across different variants offers valuable insights into the dataset's structural diversity and practical utility.
Model
Rummert legs
Taper Neto
Konual D4ARD
Fnd nor end
Nitertoa Conramn Sunn
Emagl meti
Figure 16: Constraint Type Distribution of RECAST-Test with All Constraints.
Yotal
Rule-Baadd
Model Rared
Empunal parets pon sentitiggg
Purepoo 2010
Lewa souns
Greal
Kryw
Rale toro
Kale Freing conecs
Al Seweri aec
Figure 17: Constraint Type Distribution of RECAST-Test with 5 Constraints.
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Hole-Boodd
Arpona Bictc
Treak Witee
Lange No
Roaert
Sang our
Breal on (210)
Brae 2000
Figure 18: Constraint Type Distribution RECAST-Test with 10 Constraints
Total
Longle
Rtul-Baoodd
Model-Haad
Eeghh siinec
Sme Jove 2010
Longle
Tane BLinn
No. Coeere 3030
Figure 19: Constraint Type Distribution of RECAST-Test with 15 Constraints.
C HUMAN EVALUATION
this study, three stages of human evaluation were conducted during the data generation process: constraint filtering, optimal instruction selection, and optimal response selection. The evaluation standards and results for each stage are as follows.
C.I EVALUATION FOR CONSTRAINT FILTERING
During the constraint filtering process, GPT-4c was used automatically filter out constraints that not adhered to the
by the corresponding responscs. After the filtering process, we obtained 355,751 high-quality constraints. accuracy filtering process, we conducted human evaluation verify whether the filtered constraints were truly not followed. The evaluation results indicated that there was a high level of consistency, reaching 90.1 %, between the judgments of human experts and the model's determinations. These results are summarized in Table
C.2 EVALUATION FOR OPTIMAL INSTRUCTION SELECTION
To select the optimal instruction, we employed multiple models to rewrite the original instructions. Specifically. we utilized four models-GGTT-4.. Qwen-Plus" DeepSeek- v and Doubao- 15--Po-32k-250111-2 versions of the instructions. These models ranked the instructions according the criteria outlined below. The optimal instruction was then determined using the Borda algorithm. To ensure the accuracy of the model-selected instructions, we conducted a human evaluation where experts selected the optimal instructions based on the same criteria. We then
generate alternative
https://plat openai comeee///eem//ee////////llllll
https://bailian wtw.:iaanncoooo.alllla....m
http:://plaatomm.ddeeee....
htgps///cossolle..oceennne.e...
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compared the consistency between the were based on the following two criteria:
and
instructions. Both LLM and human evaluations
Clarity of Requirements: Whether the instruction is clear and actionable.
Language Fluency: Whether the instruction is grammatically correct and easy to understand.
The human evaluation results showed that the best instruction selected by I.I.M and the best instruction selected by human experts were consistent 94.6% of the time. These results also summarized in Table
C.3 EVALUATION FOR OPTIMAL RESPONSE SELECTION
To obtain high-quality responses, we employed four models GPT-4o, Qwen-Plus DeepSeek-Ve V3, and Doubao- 1.00--1.5-PP 1.5-Pro- 32k-250115 to generate responses to the complex instructions we constructed. To select optimal response, these four L.LMs ranked the responses based on the following five criteria:
Instruction Adherence: Whether the response follows the constraints of the instruction.
Helpfulness: Whether the response fully addresses the user's 's request.
Accuracy: Whether the information correct and reliable.
Clarity: Whether the response is well-structured and casy to understand.
Conciseness: Whether the response is concise and free of unnecessary details.
After ranking the four responses using LLMs, we applied the Borda algorithm to select the optimal response for inclusion in our dataset. To verify the accuracy of the LLM-selected optimal response, had human experts select the optimal response based on the same criteria and then compared the consistency between the LLM-sclecter and human- suman--elected optimal responses. The results indicated that the optimal response selected by the LLM was consistent with the optimal response selected by human experts 95.7 % of the time. These results are summarized in Table g
C.4 IMPACT OF BASEL VALIDATOR NOISE
The human evaluations in Appendix C. and Table g show that our model-based validators achieve over 90% agreement with human experts, indicating that the verification signal is generally reliable. Beyond these aggregate agreement numbers, also argue that RLVC is intrinsically robust to residual validator errors.
First, the RLVC reward for each trajectory is computed as the mean satisfaction over dense set of constraints (typically> > per instruction). This averaging naturally attenuates the influence uncorrelated false positives and false negatives: occasional misjudgments on individual constraints are diluted when aggregated across many constraints, so the overall reward signal remains stable. Second, policy-gradient methods such as GRPO optimize expected returns over large batches and are known to tolerate moderate levels of reward noise.
This robustness is also consistent with recent RLHF findings, which show that reward models with only moderate agreement with human preferences (often around 60-70%) are already sufficient to drive effective alignment improve downstream behavior(Chen et al. 20044 TTyen et al. 2024.. In contrast, our model-based validators provide a substantially higher-fideliy signal (over 90% human agreement). If the noise level were catastrophic. we would expect training instabilities or systematic degradation (e.g.. learning spurious patterns reward hacking). Instead, we observe steady improvements model-based satisfaction rates (MSR) and overall success rates (OSR) in our experiments, suggesting that RLVC successfully extracts the underlying semantics of the constraints despite the presence of minor verification errors.
Moreover, our model-based validators only make binary (satisfied/ / not satisfied) decisions rather than assigning graded scores, which reduces the difficulty of the LLM-as-a-judge task and improves the stability and interpretability of the verification signal. This design choice is aligned with a growing body work that uses LLMs. often via rubrics or checklists, to evaluate whether responses satisfy structured criteria, and reports that such LLM-based evaluations can be robust and practical in diverse text gext-generation (ooti et al.) [2024] Loe ct al. 20025. Together with our human- -LM agreement analysis, these results further support that the residual noise in our madel-based validators is moderate and that RLVC safely leverage such validators reward sources.
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Table 8: Summary of Human Evaluation Results
Evaluation Item
Standards
Consistency (%)
Filtering Accuracy: Whether the response indeed violates the filtered constraints.
Constraint Filtering
90.1
Clarity of Requirements: Whether the instruction is unambigu- and directly actionable.
Instruction Voting
94.6
Language Fluency: Whether the instruction is grammatically correct and easy understand.
Instruction Adherence: Whether the response follows the given instruction.
Whether the response fully addresses the user's request.
Helpfulness:
Response Voting
95.7
Accuracy: Whether the information correct and reliable
Whether the response is well-structured and easy to understand.
Clarity:
Conciseness:
Whether the response is efficient and avoids un- necessary details.
D EXPERIMENTAL DETAILS
D. BASELINE
We conduct comparisons against cight representative datascts specifically curated to improve the instruction instruction-followingg capabilities of language models, namely:
Constructs GPT-4-refined dataset with hierarchical constraint instructions employs curriculum tuning to enhance complex instruction-follooingg
Conifer (Sun al. 2024):
Crab QQ
2024 Enhances LLMs by back-translating model responses into constraint-rich instructions
using stronger teacher model.
I-SHEEP (Ling al.
2024 Aligns LLMs from scratch via iterative self-generated instructionnresponsee
tuning without external supervision.
MUFFIN [Lou al.
2023: Diversifies instruction data by scaling input facets per task, yielding more robust
multi-faceted alignment.
ShareGrT Provides real user-assistan dialogues large-scale resource for training conversational Instruction-follloinngg
Evol-Instruct (Xu et al. 2024 Generates complex instructions through iterative rewriting, producing progres-
sively harder
pairs.
Suri (Pham et al. 2024): Back-translates long-form texts into multi-constraint prompts, improving structured generation over extended contexts.
Tülu 3 Persona (Lambert et al. 2024 Synthesizes consttaiit-follloviii data from diverse persona prompts
to enhance controlaale instruction tuning.
tered
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D.2 GENERAL CAPABILITY EVALUATION BENCHMARK
We employ four diverse benchmarks spanning different aspects of language understanding and reasoning:
FollowBench (Jiang al. 2024b) comprehensively evaluates dnttruction-folloinng capability through five types of fine-grained constraints (Content, Situation, Style, Format, and Example) using a multi-level mechanism that incrementally adds constraints to initial instructions. This benchmark systematieally measures models' ability to satisfy multiple constraints simultancously, with difficulty levels ranging from simple single-constrain instructions to complex multi-constraint scenarios. The evaluation employs metrics including Hard Satisfaction Rate (HSR) for complete constraint satisfaction and Soft Satisfaction Rate (SSR) for partial compliance.
IFEval (Zhou et al. 2023) assesses models' ability to follow explicit formatting instructions through strict, verifiable metries rather than subjective content evaluation. The benchmark focuses precise adherence specific requirements such keyword inclusion, format specifications, and structural constraints. This allows for rigorous, code-based evaluation that eliminates ambiguity in measuring instruction compliance.
GPQA (Rein et al. 2023) features expert-crafted questions from PhD-level domain specialists in biology, physics, and chemistry designed to be challenging for non-experts yet accessible to specialists. The dataset undergoes rigorous validation to ensure both factual accuracy and appropriate difficulty levels. Access restric- tions and gating mechanisms protect against data contamination, maintaining the benchmark's 's integrity for evaluating advanced scientific knowledge.
MuSR (Sprague et al. 2024) tests models' ability to integrate reasoning with long-conter understanding through algorithmically generated complex problems averaging 1,000 words in length. The benchmark includes murder mysteries, object placement puzzles, and team allocation optimization tasks that require maintaining coherent reasoning across extended contexts. Few models achieve above-random performance, making it an effective discriminator of advanced reasoning capabilities.
D.3 DETAILS OF DATA GENERATION
We utilize Tülu 3 Persona if as seed data, which contains instruction following tasks in various real-world scenarios. We employed Large Language Models (LLMs) extensively throughout the data generation pipeline, encompassing constraint generation, constraint filtering, instruction rewriting, optimal instruction selection, response regeneration, and optimal response selection. Specifically, for constraint generation and filtering, we utilized the API service deepseek-V3. In the instruction rewriting phase, we leveraged the API services of four distinct models: GPI-4o, Qwen-P1 us, DeepSeek-V3, and Doubao- 10-55--P-32--2222
These models generated four alternative versions of complex instructions. Subsequently, the selection of the optimal instruction involved ranking these four versions using the same set of four models. The top-ranked instruction was then chosen for the subsequent response regeneration step. Similar instruction rewriting, we again invoked the API services of GPT-4o, Qwen Plus, SeepSeek-Vi and Doubao-1. 5-Poo-32k 32k-250115 to generate four candidate respouses based on the selected optimal instruction. Finally, these four responses were ranked using the same four models, and the highest-ranked response was adopted as the final answer within our datasct.
During all API calls, we maintained consistent parameter settings: temperature=0, top_p=l, and n=l. We deliberately set the temperature to 0 prioritize the accuracy of the generated data, which is paramount complex instruction-followingg tasks. While the diversity within our dataset stems from the rich semantics inherent in real-world responses, maintaining accuracy during constraint generation, instruction rewriting, response regeneration, and the selection processes for both instructions and responses our primary concern Therefore, a temperature of was chosen to maximize the determinism and factual correctness of the LLM outputs at each stage of the data generation process.
D.4 DETAILS OF SFT
This section details our model training configuration based on the LlamaFactory (Zhngg et al. 2024) framework. We employed Supervised Fine- ThnenTunig (SFT) with maximum sequence length of 4096 tokens. For optimization, we used
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a linear learning rate scheduler with a peak learning rate of 2.0e-5, 3% warmup ratio, and trained 3 epochs. We configured training with a per-device batch size of 4 and gradient accumulation steps of 32, resulting in an effective batch size of 128. To optimize training efficiency. we utilized DeepSpeed with ZeRO stage 3 optimization and bíloat16 mixed precision. All experiments were conducted on a cluster of 8 NVIDIA H800 GPUs.
D.5 DETAILS OF RLVC
Policy Optimization We employ Group Relative Policy Optimization (GRPO) (Shao et al. 2024 learning algorithm. GRPO generates multiple candidate responses for the same instruction and computes advantage estimates through intra- comparisons. allowing the model learn the relative quality differences among various responses to the same instruction.
policy
Concretely, for every input instruction z, the current policy тy samples G candidate responses { and obtains scalar reward r; for each. We then compute the group mean μr and standard deviation and derive standardised advantage A, which measures how well each response performs relative to its peers.
During policy updates, GRPO maximises the following objective:
(o) P(x)) (n u (14))
(4)
min (rieAst, crip 1 1 1 1+6) A..) DKL [To med] }
Bi
where:
1 G 2
To(Ui, x. c1. (R. 4)2, A, R (0)
(5)
Te (y6. Ir Mi,ce)
By combining constraint- speciifc rewards with GRPO's comparative learning approach, framework provides the model with clear guidance on how to improve constraint satisfaction across diverse instruction types.
For reinforcement learning, we implemented our RLVC approach based on the VeRL (Sheng et al.] (025) framework with Group Relative Policy Optimization (GRPO). Our configuration used a learning rate of Te-6 with maximum sequence lengths of 1024 tokens for both prompts and responses. We employed batch size of 512 with PPO mini-hatches of 128 and micro-batthess of 16 GPU. For optimization stability, incorporated KI. divergence regularization with coefficient of 0.001 using the low-variance KL implementation. while enabling gradient checkpointing for memory efficiency. The rollout process utilized tensor model parallelism with a size 2 vLLM for acceleration, generating 16 candidate responses per prompt with 60% GPU memory utilization. We integrated our custom constraint-specific reward functions through the thread-based reward manager with asynchronous reward calculation. All experiments were conducted 8 GPUs for 200 optimization steps.
Implementation Details
To better understand the optimization hehaviour of RIVC, we track the evolution of both the model-based and rule-based rewards, together with their corresponding satisfaction rates, during the early phase of RL training. (epoortsssoossss the recorded values at steps 50, 100, 150 and 200.
Training Dynamics
The observed dynamics can be characterised in three phases. First, an early surge (0-100 steps): the model rapidly increases its model-based reward as it captures set of relatively easy model-based constraints, accompanied by gradual improvements in rule-based satisfaction. Second, slow-down phase (after 100 steps): once these low- www-hanging model- based objectives are largely addressed, optimization attention shifts toward more difficult, often rulc- base- objectives, causing the model-based reward to plateau or decline slightly, while rule-based metrics begin to improve more noticeably. Third, the overall trend: despite the plateau in the model-basee reward, both MSR and RSR show mild upward trajectories, indicating that the model is improving its aggregated ability to satisfy an increasingly challenging mixture of constraints.
Notably, the scale of improvements differs across metric types: model-based reward increases modestly by about 8% relative (from 0.411 to 0.415 at peak), whereas rule-based tehurh-though starting at much lower absolute
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Table 9: Reward metrics and constraint satisfaction rates during the first 200 RLVC optimization steps.
Step
50
100
150
200
Model-based reward
0.411
0.428
0,445
0.431
0.415
MSR (%)
54.0
56.5
57.5
56.0
55.5
Rule-based reward
0.011
0.022
0,016
0.030
0.018
RSR (%)
4.8
5.5
5.4
5.8
10.9
value-doubles in magnitude and its satisfaction rate (RSR) grows from 4.8% to 10.9%. This contrast highlights the higher difficulty but also the meaningful progress in handling rule-based objectives. These dynamics suggest that RLVC first exploits readily learnable, evaelaevallublle constraints before reallocating capacity to harder objectives; consequently, monitoring both per per-constraint-te tyype rewards and aggregated metrics is important for diagnosing progress and for informing choices such as reward balancing, curriculum scheduling, or extended training.
D.6 ABLATIONS AND ANALYSIS
Impact of constraint type during complex instruction learning. To understand the contribution of different constraint types, we conducted experiments using RECAST with only model-based or only rule-based constraints, the results shown in Figure 21 Results indicate that constraint type specialization affects performance metrics. Models trained with only model-based constraints demonstrate reduced performance on RSR. while maintaining competitive MSR scores. Conversely, models trained with only rule-based constraints exhibit decreased model-based constraint satisfaction capabilities, particularly evident in higher difficulty levels. This specialization effect highlights the importance of diverse constraint exposure during training. These findings underscore the necessity of incorporating both kinds of constraint types during model training to ensure robust performance across different complexity levels.
Generalization across constraint types. To further assess whether RECAST models can handle constraint types beyond those explicitly used during training. conduct held-out-type analysis using Llama-3.1 1--8B. Concretely, we compare three training configurations: (i) RECAST-30K (all types). which uses both model-based and rule-based constraints; (ii) Only Model-based basee Construints, where the model is trained solely on model-based constraints but evaluated on both MSR and RSR; and (iii) Only Rule-based Constraints. where the model is trained solely on rule-based constraints but again evaluated both MSR and RSR. We additionally include strong instruction- follooinn baseline (Tülu 3 Persona IF) that does use RECAST-30K for comparison. The numerical results are shown in Figure 21
The results reveal two key trends. First, a model trained only on model-based constraints still achieves competitive RSR and clearly outperforms the baseline on rule-based constraints. Symmetrically, a model trained only on rule-based constraints attains strong MSR and surpasses the baseline on model-based constraints. Second, using all constraint types yields the best overall performance, but removing Together, these findings suggest that RECAST training does not merely overfit to specific constraint templates; instead, it encourages the model to acquire more generalizable instruction-folle follloiinnooowiggggg behavior that transfers across different familics of constraints, providing partial evidence toward robustness to novel constraint types.
type does not cause a catastrophic drop on the other type.
Impact of constraint number during complex instruction learning. To investigate how constraint quantity affects instruction-foll following capabilities, we trained variants of RECAST-30K-SFT with different maximum constraint limits (5, 10. and 15) and evaluated their performance across all difficulty levels, the result can found Figure 20 Results demonstrate a clear alignment be- tween training constraint quantity and evaluation performance at corresponding difficulty levels. Notably, the complete RECAST-30K TOKASTT-3K-SPP SFT model maintains competitive performance across all difficulty levels despite not being special- ized for any particular constraint quantity. This balanced performance profile
Marwerrs 3
Nevelt
Tewe12
Figure 20: Impact of constraint number during complex instruction learning.
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Liama-3.1-88 (MSR)
Llama-3.1-88 (RSR)
25
RECAST-30K
90
Only Model-based Constraints
20
Only Rule-based Constraints
Baseline(TTainee
15
Score
10
60
5
45
0
level1
level2
level3
level4
level1
level2
level3
level4
(1)
(2)
Figure 21: Impact of constraint type during complex instruction learning.
Table 10: Average results for Llama-3.1-8B and Qwen2.5-7B across different RECAST components.
Average
Configuration
Llama-3.1-8B
Qwen2.5-7B
RECAST-30K
30.54
31.25
Response-Only Enhancement
27.00
25.54
Instruction-Only Enhancement
18.33
13.88
Without RECAST
26.96
26.83
suggests that exposure diverse constraint quantities during training enables effective generalization across varying levels of instruction complexity.
To isolate component contributions, we evaluated RECAST variants with specific elements re- moved[Table TTO.. Complete RECAST-30K consistently outperforms all ablated configurations. Instruction-Onil En hancement exhibits the most significant performance degradation (12.21% and 17.37% decreases for Llama and Qwen), revealing that constraint- -uggrente responses without corresponding response modifications create instruction resiioos-----eee inconsistencies that confuse the model, substantially hampering performance. Response-Only Enhancement performs comparably to Without RECAST, suggesting that constraint specification in instructions drives most performance gains. These results confirm that RECAST's complete instruction-response synthesis pipeline essential for optimal constraint satisfaction capabilities.
Impact of specific components of RECAST.
Binary evaluation for model- constraints. Some high-level model-basee constraints, such as Helpfulness, may in principle support finer-grained distinctions than simple binary satisfied not satisfied label: responses both helpful while differing noticeably in quality. learned reward model produces graded scores could better capture such nuances and enable more fine-grained optimization. this work, however, our primary focus is on whether a model can simultancously satisfy multiple constraints within single prompt, i.c., the breadth of constraint satisfaction across complex instruction. To keep this focus clear, we deliberately relax the requirement the depth of satisfaction for any single high-level constraint and adopt binary criterion the per-constraint level. In other words,
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there inherent trade-off between modeling the breadth of multi- constraint satisfaction and capturing very fine fine-gras grained differences within each individual constraint, and our design in this paper prioritizes the former. Exploring combinations of RLVC with more ine-grained, scalar reward models for certain model-based constraints is an interesting direction for future work.
Applicability of RLVC to reasoning-style base models. RLVC is conceptually agnostic to the choice of base model: it only requires that, given instruction with verifiable constraints, the model produces final response that can checked by our validators. In this sense, the framework applies equally well to reasoning-style base models and to non-reasoning models such as Qwen-2.5-76 2.5-BB and Llama-3. 1-8B used in our experiments. For many reasoning models, the interface consists of a prompt, an internal (possibly explicit) "thinking" trace, and final answer. Existing reasoning-oriented training pipelines typically supervise the final response while allowing the model to freely choose its internal reasoning. RLVC fits naturally into this paradigm: resulting reward can used train reasoning models in exactly the same way, without requiring access to or supervision over their internal thinking process. Looking ahead, one could also extend RLVC to explicitly supervise the thinking process itself, for example by augmenting data with "how to satisfy each constraint" reasoning traces and defining verifiable constraints over these traces. We view such tighter coupling between verifiable constraints and chain-of of-thought supervision promising future work.
constraint verifiers operate the final response, and the
E CONSTRAINT TAXONOMY
Prior to generating constraints, we constructed comprehensive constraint laxonomy composed of two categories: rule-based constraints and model-based constraints. The constraint categories include 19 types, with definitions and representative examples shown in Table and Table 12 These types serve as the basis for generating diverse and realistic constraints.
E.I RULE-BASED CONSTRAINTS
Rule-based constraints are objective requirements can verified using deterministic methods, such as Python functions. These constraints are structural, lcxical, or quantitative in nature, such as paragraph count, keyword inclusion, and word limits. To generate these constraints, we implemented nine rule-based extractors that identify verifiable propertics from text. These extractors analyze syntactic patterns, keyword frequency, numerical values, and other measurable elements produce concrete constraint instances.
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Table 11: Rule-basee based Constraint Types
Constraint Type
Definition
Examples
The response is required to adhere a specific length.
Respond in roughly 100 to 200 words.
Length
The response is required follow particular format.
Format ingrodients bulleted list.
Format
The response is required to be written in spe- cific language
Language
Write in English.
uppercase/lowercase.
Incorporate "The Civil Rights Act 1964" your answer.
The response is required to include specific words or phrases.
Keyword
The response is required to contain numerical patterns.
Begin your response with the word "dear".
Start With
The response is required to start with specific
End your response with the word "HARMONY".
End With
word or phrase.
The response is required to be in all capital let- ters.
Type everything in CAPS LOCK.
All Upper
The responses are required to be in all lowercase
Use only small letters your answer.
Lower
letters.
The response is required to contain numerical patterns.
Avoid commas entirely in your answer.
No Commas
E.2 MODEL-BASED CONSTRAINTS
Model-based constraints are subjective and require semantic interpretation or qualitative judgment. These involve higher-level aspects such as style, tone, emotion, and content relevance. We developed taxonomy of 10 constraint types encompassing semantic fidelity, stylistic nuance, tonal variation, and domain- specific content requirements. These were used guide the I.LM in generating rich and context-sensitive constraint instances for each response.
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Table 12: Model- based Constraint Types
Constraint Type
Definition
Examples
Maintain formal and academic tone.
Tone
The response is required to use a specified tone.
The response is required to convey certain emo- tion.
Convey nostalgia and hopeful anticipation.
Emotion
Write in poetic style with consistent AABB rhyme
The response is required to reflect a particular style.
Style
scheme.
The response required to focus on verifiable facts or imaginative content.
Include only medically verified items.
Factuality
Provide actionable advice for emergency preparedness.
The response is required to provide useful infor- mation.
Helpfulness
Mention key testimonies from friends and financial experts.
The response required to contain explicit ex- amples.
Example
Base the response on legal trial procedures and criminal case details
The response is required to be generated based background information from specific field.
Background Info
Adopt the role of cancer researcher proposing a study.
The response is required to simulate specific role.
Role Playing
Focus exclusively on psychological effects of intense training on children.
The response is required revolve around particular subject or theme.
Topic
Frame the response public announcement for hotel redesign.
The response required to be generated based on specific scenario or setting.
Situation
E.3 TEMPLATES FOR RULE-BAS BOLED CONSTRAINT GENERATION
generate rule-based constraints, we designed set of eight rules and associated rulc-based evaluation functions, which systematically assess responses within the seed datasct. When a response exhibited characteristics defined by our rules, we tailored a constraint for it. This constraint was not only preciscly adhered by the response but also had corresponding functions for verification. To prevent the constraints constructed based on rules from being overly uniform, we designed multiple templates for each type of constraint. When generating specific constraints for response, randomly selected a template to ensure the diversity the constraints. The templates each type of constraint are as follows.
E. TEMPLATES FOR LENGTH CONSTRAINTS (WORD LEVEL)
The templates for word-level length constraints summarized in Table 13 These templates cover three types of rules: Approximate, Below, and Range. Each rule type has multiple templates to ensure diversity.
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Table 13: Templates for Word-level Length Constraints
Rule
Description
Templates
Use around ( words.
Limit your response approximately words.
Aim for around () words in your answer.
Whether the word cou- of the response is ar- ound given numb-
Keep your answer close { words.
Approximate
Provide detailed response of approximately words.
Your answer should be about () words, plus or minus 20%.
Aim for approximately () words.
Keep your answer around ( words.
Keep the answer under () words.
No more than () words.
Do exceed words.
Whether the response contains fewer words than specified maxi- mum.
Strictly limit the answer to () words.
Below
Stay within { words.
words maximum.
Use fewer than () words.
Cap your response at { words.
Answer in {} words less.
Limit the response to (}+) words.
Respond roughly () () words.
Target response hetween (} and () words.
Whether the response length falls within s- pecified word range.
Answer in approximately () ()-() words.
Range
Keep the response between () and [] words.
Aim for () to () words in your reply.
Limit your answer to a length of [-() words.
Adhere word count of () ()-
E.3.2 TEMPLATES FOR LENGTH CONSTRAINTS (SENTENCE LEVEL.)
The templates for sentence-level length constraints summarized in Table 14 These templates cover four types of rules: Exact, Approximate, Below, and Range. Each rule type has multiple templates to ensure diversity.
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Table 14: Templates for Sentence-level Length Constraints
Rule
Description
Templates
Provide exactly () sentences in your answer.
Use exactly
()
sentences in your response.
Your response must contain exactly sentences.
Whether the number of sentences in the
Exact
Strietly use | sentences your answer.
sponse is exactly pecified.
Adhere limit of exactly {} sentences.
Structure your answer in exactly | sentences.
Craft ( |-sentence response.
The answer shall comprise exactly sentences.
Aim for approximately { sentences (+2).
Your answer should around () sentences, give take few.
Whether the number of sentences the re- sponse is approximat- cly equal to specifi- ed number.
Target around () sentences in your response.
Approximate
Keep your answer to roughly () sentences.
Respond with upproximately {} sentences.
The response should consist of approximately () sentences.
Limit your response to () sentences.
Use no more than () sentences.
Whether the number of sentences in the re- sponse does not exce- ed specified maxim- um.
Do not exceed {} sentences in your response.
Below
Cap your reply at { sentences.
Stay within () sentences.
Adhere to maximum of { sentences.
Keep your answer to ()-() sentences.
Respond sentences.
Provide response of (}-() sentences.
Whether the number of sentences the re-
Aim for response between () and () sentences.
Range
sponse falls within a specified sentence ra- nge.
Keep your reply within the range | |-() sentences.
The response should comprise ()-() sentences.
Maintuin sentence count between () and (}-
Provide consisting of roughly () ()
sentences.
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E.3.3 TEMPLATES FOR OTHER CONSTRAINTS
The templates for other constraints summarized in Table 13 These templates seven types of rules: Format, Keyword, Start With, With, Uppercase, Lowercase and Commas. Each rule type has multiple templates ensure diversity.
Table 15: Templates for Other Constraints
Rule
Description
Templates
Format
Respond in "{I" format.
Whether the response follows specified ou-
Format
Format your answer as valid "()".
Iput format as JS-
ON, list, paragraph, etc.
Provide the output strictly in "()" format.
Keywords
Include the keywords "()" () times in your response.
Whether the response includes a specified eyword a specified nu- mber of times.
Your response must feature "()" () times.
Keyword
Use "()" () times when responding.
Ensure your answer contains () "()".
Incorporate () "()" into your answer.
Position Specific Strings
Begin your response with the word "()".
Whether the response begins with specific- word or phrase.
Start With
Start your answer with "(]".
Open your reply using ()" as the first word.
Whether the response ends with a specified word or phrase.
End your response with the word "[]".
End With
Make the last word of your reply "()".
Your response must terminate with "()".
Letter Case
Write your entire response in UPPERCASE.
Whether the response written entirely in u- pperease letters.
Use ONLY CAPITAL LETTERS in your answer.
Uppercase
Type everything in CAPS LOCK.
Write your entire response in lowerease.
Whether the response written entirely in owercase letters.
Lowercase
Use only small letters your answer.
Avnid any capital letters in your reply.
Punctuation
Do not any commas in your response.
Whether the response excludes all commas from the output.
No Commas
Avoid commas entirely in your answer.
Exclude all commas from your output.
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F MAIN PROMPTS
Prompt Template for Constraint Generation via LLM Figure illustrates the prompt template used generate constraints from responses of seed dataset. Given response and a iist of constraint categories, the L.L.M instructed generate concrete, imperative-stlle constraints that can be attributed to each category. The template guides structured dictionary. where each constraint type maps written in command form (e.g.. "use least words". "respond in formal tone"). "). This prompt plays a central generating large number of diverse, multi-gra multi-grannuar real-world constraints, enabling systematic conversion from abstract categories to enforceable constraints.
the LLM
specific constraint instances
Prompt Template for Constraint Generation LLM
Given An LLM response known to be generated under prompt containing specific constraint calegories and the constraint categories present in the original prompt. Analyze the response generate concrete imperative-style constraints for each constraint calegory below.
The LLM Respunse: [LLMM RESPONSE)
The uriginal prompt has the following constraints:
(CONSTRAINTS EXPLANATION)
Output format:
Return dictionary where:
Keys are constraint type
Values are lists of full imperative-frrm constraints such as such as ||"Constraint typel""" Conerrte Constraintl", "tarrrinnt"""""oeeee" "Concrete Constraint2"]. "Constraint typeZ"("Concrete Constriint1", "iriintt""""CCnoe"""" "Concrete Constraini2", 'Concrete Constraint3")]
Don't t output any additional text or explanations:
Figure 22: Prompt Template for Constraint Generation via L.MM
Prompt Template for Adding Constraints to Original Instructions Figure 23 presents prompt template designed augment original instructions with additional constraints in fluent and semantically consistent manner. Given initial instruction from the seed dataset and dictionary of constraints, the LLM prompted to modify the instruction by incorporating any missing constraints while preserving its original intent. This prompt is particularly useful for generating constraint-rics rich instruction datascts, as it ensures that generated instructions explicitly encode the intended exccution requirements without altering their core meaning. The output single revised instruction that scamlessly integrates all constraint conditions.
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Prompt Template for Adding Constraints to Original Instructions
Task: Constraint-A ware Instruction Expansion
You will be vin:
1. user instruction (to be given to another Al system)
2. A dictionary of constraints (require ments for how the instruction should be executed)
Your task is MODIFY USER INSTRUCTIONby:
Analyzing each constraint in the dictionary
each constraint NOT already present in user instruction:
Add it to the instruction in natural, fluent way
Ma intain the original meaning and flow constraint is a satisfied by the instruction:
Leaveit unchanged
Return ONLY the final modified instruction
CRUCIAL NOTES:
You NOT to apply the constraints to the instruction text itself
You are ADDING REQUIREMENTS to instuuctutioon
Preserve the original instruction's intent and wording where possible
Example 1:
User Instruction: "Write poem about the ocean"
Constraints: (f"lnggh": "at least 20 lines", "style": "haiku"))
Output: "Write poem about the ocean that least 20 lines long and haiku style"
Example 2:
User Instruction: "Summarize the text in 300 words" Constraints: (f"lenghh: "200-400 words", "tone"; "professional")
Output: "Summarize the text in 300 words using professional tone"
Now process the following:
### User In Instruction:
[USER INSTR]
*## Constraints Dictionary:
[DICT] [ONSTRAINSS
Return ONLY the final modified instruction:
Figure 23: Prompt Template for Adding Constraints to Original Instructions
Prompt Template for Ranking Multiple Instructions Figure 24 shows the prompt template used for voting- based instruction selection. this setup. the LLM presented with candidate instructions (labeled A-D) the same task and asked to rank them based on two criteria: clarity of requirements and language fluency. The prompt strictly instructs the LLM to output only the ranked order in fixed format without any explanation, facilitating consistent and automatable comparison. This template is employed to curate high-quality instruction data by identifying the most effective formulation among alternatives.
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Prompt Template for Ranking Multiple Instructions
###Task:
I will provide four versions of user instructions (labeled A, B, C, D) for the same task. Evaluate them based on:
1. Clarity of requirements: Whether the instruction is unambiguous and directly actionable.
2. Language fluency: Whether the instruction is grammatically correct and easy to understand.
##4Output Requirements:
Only output the ranked order in the format: Best Good Fair Worst (eg., B> A D> C).
Do not include any explanations or analysis.
###Instuctionss to Evaluate:
A: [INSTR A]
B: (INSTR B)
C: [INSTR C [INSTR_D D]
###uututt
Figure 24: Prompt Template for Ranking Multiple Instructions
Prompt Template for Ranking Multiple Responses To facilitate the selection of the optimal response from pool of candidates, we designed prompt for ranking multiple response. This prompt guides multiple models evaluate and rank the responses based on predefined criteria, ensuring the selection of the optimal response. The structure of the prompt is shown in Figure 25 The prompt template in Figure includes placcholders for the candidate instructions and evaluation criteria. This design ensures that each model provides structured and comparable assessment of the instructions.
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Prompt Template for Ranking Multiple Responses
###Task:
Rank the following 4 responses (A, B, C, D) to the user instruction:
CUSSERJNSTRUCTIONN
###Evauatiion Criteria:
Instruction Adherence Does the response follow the constraints of the instruction? Helpfulness- - Does the response fully address the user's request?
Accuracy the information correct and reliable? Clarity Is the response well-structured and easy to understand? Conciseness Is the response efficient without unnecessary details?
##fResponses:
|Response A]
B: [Response_] B
C: |Response C)
D: [Response_D)
###Output Format:
Only provide the ranking in the exact format: Best Good > Fair > Worst (e.g. B> A> D> C)
Do not include explanations, analysis, or additional text. Do not deviate from the ranking format.
###Output:
Figure 25: Prompt Template for Ranking Multiple Responses
Prompt Template for Evaluating Responses systematically assess whether model response strictly adheres to a specific constraint within an instruction, we utilize detailed evaluation prompt. This prompt guides the LLM objectively determine if the constraint is met, focusing solcly on the specified constraint rather than the entire instruction. The structure and guidelines of the prompt are designed ensure precise and unbiased evaluations. The prompt is illustrated in Figure 26
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Prompt Template for Evaluating Response
Please act as an objective and fair evaluator to analyze the "model response" content and chuose "Yes" or "No" to answer whether subsequent "*cons traint" is to Please provide an objective and fair answer based on the following judgment rules:
"The ""constraint" can understood as by scoving point the "input instruction"', judging whether a specific part satisfied. Therefore, you only need consider whether the requirements stated in the ""constraint" met, without focusing whether the entire "iipput instruction" is completely satisfied. Example:
"fInput instruction": "Please geverate poem, and end with 'Generation complete."
"Model response**: 'Moonlight before my bed, perhaps frost on the ground. Lifting my head, I gaze at the bright moon, lowering my head, think of my hometown."
""oonstraint* "The poem writtun
Bai."
For this constraint, although the model response did not complete the ""input instruction" requirement end with "Generati on complete," met the ""constrain!" requirement, as the poem is Bai's "Quiet Night Thoughts," so answer should be: "Yes." but entire "Input instruction" not completely satisfied, the
For situations where the "constraint requirements answer should be "Yes."
-Yes: Please check whether the mooeel response" meets the "ccnnstaintt", fully understand the meaning of the ""onstrainn"" don't miss small details, focus only on the curzent ** connttraint", don't pay attention other requirements the "'Input instruction". The response must perfectly and adequately meet the constraint reqirements to be beevalusted as "Yes." Even if there should
error or ambiguous content, cannet be "Yes." should all be evaluated "No."
claims like "basically correct," "mostly correct,"
"conect under certain conditions. Such
No: If the text in the ""model response" cannol meet the requirements of the current ""constraint" or does not provide
informatinn
be
verify the constraint. Choose
Example: the constraint states 'The second sentence of the generated text compound sentence" but the *model response** has only one sentence. It does not provide relevant information to verify the constraint. Therefore, the answer should be 'No." Scoring details:
When evalunting whether the model response itemized, must clear bullet points numbers to be considered itemizee and evaluated as "Yes" otherwise evaluate as "No." Using only connecting words such as 'first" "then," "second, "finllly" etc. cannot be recognized as itemization and should be evaluated as "No."
(2) When evaluating whether the model response uses certain language (such as Chinese/English) for output, unless the "Inp instruction" mentions the need to use multiple languages, must use only that language to be evaluated as "Yes." IIf multiple languages are mixed (l.e. words from other langu ages appear), should be evaluated "No."
(3) If ""constriin" "taintt contains descriptions like "each" 'all." etc. the satisfaction of each object needs to be considered. If any object does not meet the requirements, it should be evaluated as "No"; only when all abjects meet the requirements can it be evaluated as "Yes."
(4) For ""constraints"* stating 'The model correclly judges" something, when evaluating whether the model correctly judged certain selection condition, necessary to judge whether model response correctly chose the corresponding branch based on ins trutiuttion requirements different selection branches "input instruction": sa Output format
Return in json format
"pon
I|I|
"Analysis": "xox",
"Answer': Yes/No
[!II
#2 Evaluation information
*"Input instruction"*
(imput)
*MModel response"*
[output]
"Constraint
(constraint)
Please analyze ze and answer whether the "*model response" satisfies the ""constrain!*:
Figure 26: Prompt Template for Evaluating Responses
The prompt template in Figure includes the following key components:
Objective and Fair Evaluation: The LLM is instructed act objective and fair validator, analyzing the model response content and choosing "Yes" "No" to indicate whether the constraint is met.
42

---

Published as conference paper ICLR 2026
Judgment Rules:
The prompt provides clear evaluation, emphasizing that constraint should understood as a specific scoring point the input instruction. The evaluation should focus solely on whether the requirements stated in the constraint are met, without considering the overall satisfaction of the input instruction.
Examples:
The prompt includes examples to illustrate how to apply the judgment rules. For instance, even the model response does not fully satisfy the input instruction, it can still meet the constraint, resulting in a "Yes" answer.
Scoring Details:
The prompt outlines specific scoring details various types constraints, such as itemization, language usage, and the presence of specific elements in the response.
Output Format: The LLM instructed return the evaluation in structured JSON format, including analysis and clear "Yes" "No" answer.
This prompt ensures that cach constraint is evaluated rigorously and consistently, providing reliable method for assessing the adherence of model responses to specific constraints.
LLM USAGE STATEMENT
LLMs did contribute to the research ideation, experimental design, analysis, manuscript writing. All conceptual and textual contributions are solely attributable the authors.
43


Reviews:
[{"review_id": "1Nput4ZjtN", "reviewer": "Reviewer_jkYB", "summary": "This paper introduces RECAST, a data-synthesis framework that mines verifiable constraints from existing instruction-response pairs and rewrites the original instructions to include many more constraints. The authors use this pipeline to build a training dataset RECAST-30K (~ 30k examples; 19 constraint types), with both rule-based and model-based validators (LLM-as-judge) attached to each constraint. They also propose RLVC (Reinforcement Learning with Verifiable Constraints) which turns per-constraint satisfaction into a fine-grained reward and optimizes policies via GRPO. They also introduce a new benchmark, RECAST-Test, with 4 difficult levels. The experiments show sizable gains in hard satisfaction rate on RECAST-Test benchmark, as well as improvements on IFEVAL and FollowBench, while largely preserving general knowledge performance (evaluated with GPQA and MUSR). Human evaluations support the quality of constraint filtering and selection.", "questions": "1) Can you clarify what exactly is the novelty of RLVC in comparison with RLVR methods? \n\n2) Can RECAST-trained models handle novel constraint types not present in RECAST-30K? Please add an experiment with held-out constraint categories (Weakness 2)", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- **Clear motivation and problem:** The paper targets a real gap, LLMs’ performance drops as the number of explicit constraints increases, and existing SFT datasets usually don’t cover a high number of constraints. \n\n- **The pipeline is complete and reproducible:** The paper details each stage (constraint extraction, instruction enhancement, response synthesis, validation), gives prompts/templates, and reports human agreement metrics. RLVC is specified with GRPO objective and training setup. This level of detail supports reimplementation. The authors promised to release code, data, and trained models upon acceptance. \n\n- **Evaluation setup is strong:** The primary metric, HSR, is well defined (satisfies all constraints simultaneously) with subtype metrics RSR/MSR for rule/model-based subsets. The proposed benchmark RECAST-Test (cover 4 difficulty levels) and it is associated with two external sets (IFEVAL, FollowBench) for the task. Also, the work included a general ability evaluation with GPQA/MUSR, which is important.\n\n- **Empirical gains & informative analyses:** RECAST-30K improves complex instruction-following against multiple baselines, with RLVC further improving especially at higher difficulty levels. The paper includes informative ablations (constraint type only, quantity caps, component removals) and training dynamics for RLVC that illustrate different learning curves for rule- vs. model-based constraints. It also contains Human evaluation to back up the automated choices.", "weaknesses": "- **Lack of theoretical positioning against RLVR methods:** The paper does not situate its RL formulation within the growing line of Reinforcement Learning from Verifiable Rewards (RLVR) research, despite clear conceptual overlap. Despite the original paper of RLVR being cited [1], it is cited only in the context of the dataset. Other foundational RLVR works [2,3] and subsequent works applying the verifiable reward mechanisms to multi-constraint or format-constrained instruction-following [4,5] are not cited or compared against. As a result, the contribution of RLVC relative to existing verifiable-reward frameworks remains under-theorized: it is unclear whether the proposed per-constraint reward structure offers advantages over scalar verifiable rewards, or whether similar behavior would emerge from established RLVR baselines. Strengthening this connection and including appropriate baselines would significantly clarify the novelty and necessity of RLVC.\n\n\\* It is acceptable to not cite works that appeared less than 2 months from the submission deadline, but in that case still recommendable for the final version. \n\n\n\n- **Generalization:** Parts of the training data and evaluation share the same constraint types and verification machinery. Although external benchmarks are included, they also contain very similar constraint types. I propose the authors to present stronger isolation tests (e.g., unseen constraint types or schemas) would help mitigate concerns about over-specialization to the authors’ verification prompts and demonstrate generalization. To do that, maybe it is not needed to run new experiments, but rather present the results of tables 2 (and 1 if possible) including only constraint types not included in the trained data. \n\n\n- **Model-based validators error rate and impact:** Rule-based validators are clear, but false positives/negatives and model-based misjudgments are not deeply quantified beyond aggregate human-agreement numbers. It would be nice to have a better quantification of how those affect the performance, or if the method is robust enough to some mislabeled examples. \n\nReferences:\n\n[1] Lambert, N. et al. Tulu 3: Pushing Frontiers in Open Language Model Post-Training. COLM 2025 (appeared in Nov, 2024). \n\n[2] Su, Yi, et al. \"Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains.\" arXiv preprint arXiv:2503.23829 (2025).\n\n[3] Wang, Y., et al. \"Reinforcement learning for reasoning in large language models with one training example.\" arXiv preprint arXiv:2504.20571 (2025).\n\n[4] Peng, H., et al. VerIF: Verification Engineering for Reinforcement Learning in Instruction Following. arXiv preprint arXiv:2506.09942 (2025).\n\n[5] Pyatkin, V., et al. \"Generalizing Verifiable Instruction Following.\" arXiv preprint arXiv:2507.02833 (2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fhcbMew7nw", "reviewer": "Reviewer_FXJy", "summary": "This work proposes a framework to construct lagre-scale datasets to test Large Language Models's instruction-following ability when facing complex tasks. Different from previous works, the constructed dataset encompasses various types of constraints, including both subjective and objective ones, and features a larger number of constraints than existing datasets. Furthermore, this study adopts both supervised fine-tuning and reinforcement learning to enhance the Large Language Model's capability to follow instructions.", "questions": "I have the following questions / suggestions:\n- Would it be possible to extend the constraints numbers of rule-based constraints?\n- Why HSR metric is not used in Table 1?\n- Why the performance of RECAST-30K-RLVC on Qwen-2.5-7B become worse than RECAST-30K-SFT from time to time? (Bottom of Table 1)\n- It would be better to also test some instruction-following enhancement works. For example,\n    - Branch-Solve-Merge Improves Large Language Model Evaluation and Generation. In NAACL. 2024.\n    - Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions?. In Findings of ACL. 2025.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This submission has the following strengths:\n- The paper demonstrates clear writing and a well-structured organization.\n- The proposed dataset RECAST-30K is large in scale and contains an adequate number of constraints.\n- Experimental results have shown that training with RECAST can effectively enhance large language model's ability in instruction following.", "weaknesses": "This submission has the following weaknesses:\n- For model-based constraints, the quality depends on used large language models.\n- The count of Rule-based constraints are much less than model-based constraints.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hLlOMFSME7", "reviewer": "Reviewer_LBsv", "summary": "This paper introduces RECAST, an automated framework for generating instructions comprising far more constraints than those in current benchmarks and corresponding responses. Using this framework, the authors construct RECAST-30K, which contains 30K instances with diverse constraint types. The dataset is then used for training LLMs via RLVC, an optimization method that leverages the verifiable nature of constraints to provide fine-grained reward signals. Experiments demonstrate the effectiveness of RECAST compared with other complex instruction fine-tuning datasets.", "questions": "See Weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The framework incorporates an extended number of constraints in a single instruction, which facilitates the evaluation and improvement of LLMs' capability of following more complex instructions as the tasks for LLMs are becoming increasingly complicated.\n2. The framework is automated and scalable, providing an efficient approach of synthesizing datasets with multiple constraints in instructions.\n3. LLMs trained on data from this framework exhibit improved performance and good generalization on instruction following tasks, indicating the effectiveness of this framework.", "weaknesses": "1. Some model-based constraints may not suitable for a binary evaluation. For example, for the \"Helpfulness\" constraint, it is common for LLMs to generate two helpful responses but with discrepant levels. If both of them are judged as satisfying the constraint, the gap between the two responses will be eliminated, which is not conducive to fine-grained model performance optimization.\n2. The effectiveness of RLVC on reasoning models are not validated. Both Qwen-2.5-7B and Llama-3.1-8B are non-reasoning base models. Considering the widespread application of the reasoning model, it is necessary to validate the effectiveness of RLVC on base models with reasoning capabilities.\n3. In Table 1, 2 and 3, it seems that the percent sign of the result values is missing. And the presentation form of results is also inconsistent across different contexts (e.g. \"achieves a 31.25% average satisfaction rate\" around line 349 and \"achieves average scores of 34.64\" around line 373).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)