Prompt-MII: Meta-Learning Instruction Induction for LLMs
Authors: Emily Xiao
Yixiao Zeng
Ada Chen
Chin-Jou Li
Amanda Bertsch
Graham Neubig
Venue: ICLR 2026 Poster (2026)
Keywords: Instruction Induction
Prompt Generation
Prompt Optimization
Reinforcement Learning
Task Adaptation
Large Language Models
Primary area: foundation or frontier models, including LLMs

Abstract: A popular method to adapt large language models (LLMs) to new tasks is in-context learning (ICL), which is effective but incurs high inference costs as context length grows. In this paper we propose a method to perform instruction induction, where we take training examples and reduce them to a compact but descriptive prompt that can achieve performance comparable to ICL over the full training set. Specifically, we propose Prompt-MII, a reinforcement learning (RL) based framework to meta-learn an instruction induction model that can generate compact instructions on the fly for an arbitrary new dataset. We train on over 3,000 diverse classification datasets from the HuggingFace hub, and evaluate on 90 unseen tasks. Prompt-MII improves downstream model quality by 4-9 F1 points (10-20\% relative), matching ICL performance while requiring 3-13x fewer tokens.

Published as a conference paper at ICLR 2026
PROMPT-MII: META-LEARNING INSTRUCTION IN- DUCTION FOR LLMS
Emily Xiao, Yixiao Zeng, Ada Chen, Chin-Jou Li, Amanda Bertsch, Graham Neubig Language Technologies Institute, Carnegie Mellon University demilyy,jackke adachen, chinjoul, abertsch, gneubig) cmu. edu
ABSTRACT
A popular method to adapt large language models (LLMs) to new tasks is in- context learning (ICL), which is effective but incurs high inference costs as context length grows. tion, where we take training examples and reduce them to a compact but de- scriptive prompt that can achieve performance comparable to ICL over the full training set. We propose PROMPT-MII, reinforcement learning (RL) based framework to meta-learn an instruction induction model that can generate com- pact instructions the fly for arbitrary new dataset. We train over 3,000 diverse classification datasets from the HuggingFace hub, and evaluate 90 unseen tasks. PROMPT-MIT improves downstream model quality by 4-9 Fl points (10-20% relative), matching ICL performance while requiring 3-13x fewer tokens. Code is available /https://gitt httt::///ggttuu...mmmmm111////// Models and datasets are available at htttls:://hugggngaacc. co/ cttpe//aaaaacoooooletiiioooooo 2ttp:///rrrrommmmmmamaaaaaaaa120
alternative approach is to perform instruction induc-
INTRODUCTION
Performance vs Prompt Length
One common use pattern for large language models (LLMs) is to adapt them to specific downstream task. In supervised adaptation scenario, we are given labeled demonstra- tions Sumin
0.45
compression
0.43
n050
n-100
0.40
n-
0.38
(()+,))))== }E and are inter- ested in the problem of how to accurately pre-
0.35
n-5
0.33
dict labels for a set of test examples Siest = (z),,;;)) drawn from the same distribu- tion.
0.30
Naive Instruction In-Context Learning
0.28
PromptMil-Zero PromptMIl
0.25
There are multiple typical ways to incorporate the given examples: (1) Prompting with in- structions, where natural language task de- scription is appended to the model prefix, (2) In-context learning (ICL), which directly uses examples in Strin as context during inference, and (3) Supervised fine-tuning (SFT), which performs gradient updates on Staai to condense the information into model parameters. Each method has its advantages. Prompting with instructions is concise and efficient but requires exten- sive prompt engineering (Sahoo et al.; 2024) [Schulhoff al. 2024). ICI. achieves highly competi- tive performance but be inefficient the number of examples grows larger (Xiao et al.) 2025). SFT is efficient at test time but uses significant compute at training time, requires storage of model weights, and underperforms ICL in many cases (Bertsch et al. 2024).
10k
12k
Prompt Length (Tokens)
Figure Classification task performance aver- aged over 90 datasets, using the Llama-3.1 1-8B- Instruct model. PROMPT-MII achieves perfor- mance comparable to ICL while using 13x fewer tokens.
As method to bridge the gap between ICL and prompting, there exists instruction induction, which takes training data Swain and generates instruction I that achieves good performance. Represen- tative methods for instruction induction such as APE (Zhou et al.] 2022) and GEPA (Agrawal et al.] 2025) typically do through expensive evolutionary search algorithms at test time that generate

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Published as a conference paper at ICLR 2026
Candidate Instructions
Instruction Generator
Examples
Ins #1
Ins #2 ... Ins #N
Dataset
RL Finetuning
Reward (Macro F1)
Instruction Follower
Scoring
Ins #1
Ins Ins #N
Figure 2: Overview of PROMPT-MIL We train an Instruction Generator's general ability to perform instruction induction. At inference time, given dataset examples of an unseen task, it automatically generates reusable task instruction in a single pass, which then guides black-box Instruction Follower model to make predictions.
multiple candidates for prompts and evaluate them to choose a well perf-oerf prompt. This raises question: is there a way to perform instruction induction in a way that both effective and efficient over wide variety of tasks?
As an answer to this question, we propose PROMPT-MII, where we frame instruction induction as a meta-learning problem: instead of individually optimizing T for each individual task, we train an instruction induction policy Te that can effectively generate instructions single pass across diverse task distributions, conditioned only on the in-context examples:
= TT (
(1)
train
There are two major advantages to this approach. First, it allows Ty to share knowledge about how to construct effective prompts across wide number of datasets, instead of requiring the re-discovery of this knowledge for each dataset. Second, it has significant efficiency benefits generating an instruction / for a new dataset simply requires a single forward pass through the language model, instead of costly optimization process.
Experiments demonstrate that PROMPT-MII is highly effective. For instance, in Figure we show how PROMPT-MII can achieve performance comparable to 100-shot ICL while consuming 13x fewer tokens. the following sections, we discuss the methodological details of PROMPT-MII experimental details 3), and results and analysis 4).
2
PROMPT-MII: META-LEARNING INSTRUCTION INDUCTION
The main challenge in developing method generate instructions from a dataset S is learn ing an effective policy Ty that can generate these instructions in a way that will achieve good test performance. We use reinforcement learning (RI.) to train this policy because ground-truth dataset- instruction pairs often not available for existing public datasets. However, we evaluate in- struction quality through downstream task performance, which serves as a natural reward signal for RL. In this section, develop method for meta-learning such policy, also shown in Figure 2
2.1 TRAINING OBJECTIVE
Let (S1, S2, ... SN1 be collection of datasets that will use in the meta-learning of my. For each dataset S., we sample training examples 5c() for instruction generation and test examples s((i) for reward computation. We define meta-prompt template T($(1) )), which converts the dataset into a prompt to the model, as detailed in $ 2.2 Then, generates an instruction prompted by this meta- peetppprompt,
To (T(S(()) (((()))))
To assess the quality the generated instruction, we use a separate frozen language model LM the instruction follower. This LM then processes the test set Sies using this instruction, generating
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Published as a conference paper at ICLR 2026
results úU MMveval "Input: " "+æ, "Label:"). We task-dependen evaluation metric test examples to assess the model performance E (10) J J==1 {y, )=m)). In principle, this metric can range from classification metrics such as accuracy and macro-F1 to generation based metrics such LLM--a-a--uddee but in this work we focus classification tasks and macro-F1 target reward metric and m = 20 to balance stability and efficiency. To avoid training the model to learn the format requirement that is easily enforced manually, we add this custom format line: Only return one of these options: text. after the generated instruction, before calculating the reward. This constraint is equally added to all baseline methods we compare in the results.
Do not output "Label:" or any extra
Together, this results in a reward for our generated instruction of {N, (y)))))
R(I, S(Ss) E E(
(2)
Once we have defined this reward. it can be optimized with an RL algorithm of choice. In this work, we use Group Relative Policy Optimization (GRPO; Shao et al. (2024) and enhance the algorithm with asymmetric clipping and removal of KL loss, which has been shown to encourage more exploration YYu et 2025). Full details of the RL objective are A.l
2.2 META- PROMPT TEMPLATE
One key element of our method is the use of a meta-prompt template T that encourages the LLM to generate instructions with generalizable patterns rather than regurgitating specific examples or simply summarizing the label space.
Meta-prompt design impacting prompt quality is known phenomenon in automatic prompt opti- mization (APO) methods (Ding et al. 2025). Our ablation studies in 4.5 reveal model-dependent preferences, and accordingly, we use model-specific mneta-prompts optimized for each model, but fix the same template for training and evaluation of all baselines.
Meta-Prompt Template (Qwen)
You designing clear instruction for data annotator classify text inputs into of these labels: [aabll_names)
Here are some example inputs and their correct labels: [examples)
Your task is to write a concise instruction that:
Defines the classification task and clearly explains the meaning each label.
Provides general labeling strategies and decision rules so annotators can correctly handle unseen inputs.
Highlights common pitfalls, tricky edge cases, and misconceptions to reduce label- ing errors.
Keeps the instruction reasonably concise and focused avoid unnecessary repeti- tion or overly long explanations.
Here, [label_names] is comma comma-separated list of all of the labels in the classification dataset Spain (e.g.. "positive, negative, neutral") and (examples) follows the format: Text: "example input text here"\nLabel. sxample_label See $ A.6 for details.
3 EXPERIMENTS
3.1 TRAINING DATA PREPARATION
We collected all publicly available text classification datasets from HuggingFace, applied automated filtering, and randomly sampled training examples s(i) described in Appendix A.2 After filtering, we obtained 3 diverse datasets, which were randomly split into 3,430 for traniing and 381 for validation.
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3.2 TRAINING SETUP
We conducted training using the VERL framework (Sheng al. 2024) on two model variants: L1ama-3. 1888-Instttct and -----..---IIstruct. For each variant, we used the same official model checkpoint for both the instruction generator (Ig) and the instruction follower (La)v While LM was kept frozen at the official checkpoint, me was updated during training. For training, we used rollout size of n 5, batch size of 64, maximum response length of 1k tokens and maximum prompt length of 4k tokens. Further hyperparameter and system details are provided Appendix A.3
3.3 EVALUATION SETUP
Data We randomly select 90 datasets from the validation set for evaluation, which is disjoint from the training set. For each dataset and each n. E (5, 10, 20, 50, 100), sampled training examples, generated instructions, and applied them to 200 test examples. The context length was limited to 32k tokens. If the n examples exceeded this limit (applicable to ICL and PROMPT-MII), we used the maximum value of n that fit within the context. See A.2 for further implementation details on dataset selection, and Table 7 for full list of datasets and statistics.
Baselines We compared our method against Naive Instruction, In-Context Learning (ICL), PROMPT-MI1-Zero (untrained instruction generator), and PROMPT-MII-Zero with larger models (Llama-3. 1--45B-Instruu search-based prompt optimization methods APE (Zhou et al., 2022) and GEPA (Agrawal et al.) 2025). Since our datasets do not provide ground-truth instructions, all baselines we consider are automatic prompt generation methods. Further implementation details are described in A.4
Owen-3-2358- (338B-Instruct.. We also compare with inference-
Prompt Template for Naive Instruction Baseline
Classify the Input. Only return of these options: (label1, labelz, ... label,, Do not output 'Label:" or any extra text.
Prompt Template for ICI. Baseline
Classify the Input. Only return one of these options: [label,, labelg, label, J. Do not output 'Label:" or any extra text. Input: (Example 1}
Label: (Label 1)
Input: (Test case)
Label:
Metrics Our evaluation metric for task performance is the macro-F1 score, which is consistent with the training reward, and accounts for label imbalance. To assess efficiency, we report the prompt token length, since shorter prompts directly translate to lower inference cost and latency when deployed on LLMs. Additionally, we report win rates (the percentage of datasets where one method outperforms another) and the training curve in A.5
4 RESULTS
4.1
PROMPT-MII SUCCESSFULLY GENERATES CONCISE AND EFFECTIVE INSTRUCTIONS
RL training consistently improves instruction generation across held-out tasks, providing the first evidence that one-pass instruction induction is skill learnable by language models. As shown in
Figure 6 and Table I] Llama PROMPT-MII (trained) achieves +9% absolute FI improvement PROMPT-MIL SIFPT-MMI- -ero (untrained) at n=20 (26% relative gain), while Qwen PROMPT-Mi MII shows +5% absolute improvement (15% relative gain).
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Published as a conference paper at ICLR 2026
Training conducted with limited context length of 4k context length is able to have improvements generalized to 32k context length. Notably, Llama PROMPT-MII using n=20 examples (0.433 FI, 901 tokens) matches ICL performance using n=100 examples (0.430 FI, 11,531 tokens), represent- ing a 12.8x token reduction with no statistical difference performance, as shown in (Table 1 and Figure 3) We also compare the per-dataset win rate between ICL and PROMPT-MII, and find that both prevail in an approximately equal number of tasks FFigure 8) Appendix), PROMPT-MII has similar win rate ICL (approximately 50-50), Together, these results suggest that PROMPT-MII a strong alternative for practitioners to consider.
Llama3.1-888 1-8-1-1-r--Instcc
Qwen2.5-7B-Instruce
0.45
0.43
0.48
1-100
5=
0.45
n=50
Score
0.38
10
Score
0.43
n=20
0.35
0.40
0.33
0.38
0.35
n=5
0.28
0.33
0.25
0.30
2k
4k
6k
10k
12k
4k
6k
8k
10k
12k
Prompt Length (Tokens)
Prompt Length (Tokens)
Instruction
In-Context Learning
PromptMil--ero
PromptMill
Figure 3: Performance vs prompt length comparison across different prompting methods. PROMPT- MII (green diamonds) consistently outperforms other methods while using fewer tokens than ICL (blue triangles). Dashed lines connect ICL and trained methods for the same number of examples (n), demonstrating prompt compression while maintaining performance.
Table 1: Average Macro-Fl performance (higher is better) across 90 datasets, with average instruc- tion token length underneath (lower is better). Statistical significance markers (* < 0.05, *** p 0.001) indicate significant differences between PROMPT-MIT and ICL methods (Wilcoxon signed-rank test). n represents the number of demonstrations, which is not applicable for Naive.
Llama3.1-8B
Qwen2.5-7B
Method
n=
T==10
n=20
n=
n= 100
n=5
n=10
n=20
n=
1==000
0.253
0.253
0.253
0.253
0.253
0.303
0.303
0.303
0.303
0.303
Naive
531
531
531
531
531
609
609
609
609
609
0.347
0.385
0.406
0.424
0.430
0.363
0.403
0.431
0.463
0.482
ICL
2451
3594
5177
8206
11531
2597
3765
$390
8539
12027
0.316
0.329
0.343
0.354
0.336
0.369
0.390
0.383
0.387
0.371
PROMPT- PROMPT-MII-ZenT
709
702
709
710
715
1541
1481
1574
1538
1609
0.388
0.415
0.433
0.416
0.405*
0.409
0.434
0.441
0,432
0.424
PROMPT-MII
873
891
901
965
956
1523
1677
1807
1774
1737
4.2 PROMPT-MII OUTPERFORMS EXPLICIT OPTIMIZATION TECHNIQUES
PROMPT-MII substantially outperforms iterative prompt optimization methods despite requiring only single forward pass. As shown in Table 2] PROMPT-MII achieves 0.405-0.432 F1 compared to APE's 2.288-0.358 and GEPA's 0.296-0.347, while using much fewer I.LM calls test time (1
for PROMPT-MII vs 150 for GEPA, and 2000 for APE, see details in Appendix A.5).
Even when controlling for the meta-prompt template Table 5), APE with our meta-prompt template still underperforms PROMPT-MII-Zent and significantly underperforms PROMPT-MIL. We hypoth- esize that this relatively underwhelming performance of APE and GEPA likely stems from two factors. First, Qwen 2.5 7B Instruct is relatively small model, and it may be not have strong enough ability to reflect on its own mistakes helpfully (unlike larger models). Second, classification tasks may be challenging for iterative refinement algorithms, as they require understanding high-
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Published as a conference paper at ICLR 2026
Table Comparison of PROMPT-MII against APE and GEPA optimization methods. Performance
shown as macro-Fl scores for different model and example count (n) combinations.
Methods
Llama (n=50)
Llama (n=100)
Qwen (n=50)
Qwen (n=100)
Naive
0.253
0.253
0.303
0.303
APE
0.278
0.288
0.358
0.356
GEPA
0.296
0.299
0.346
0.347
PROMPT-MII
0.416
0.405
0.432
0.424
level patterns across distributions instead of analyzing individual errors. This pattern recognition ability critical for classification and regression, but less essential for generative tasks like QA or summarization.
To elaborate further, few concrete hypotheses for why classification tasks may be challenging for iterative refinement algorithms are: (a) Limited feedback signal: generative tasks like multihop QA emit traces (reasoning, tool outputs, etc.). Classification gives only a label/correcttess, offering little to reflect (b) Difficult credit assignment: modular generative pipelines localize emrors specific modules (in GEPA a human defines the modules). Classification does not have modules, so edits global. (c) Noise and overfitting: iterative refinement methods small minibatches for each refinement step (e.g., GEPA uses 3 examples). For classification tasks, the very few examples may represent the overall distribution, so recent edits may override/corrupt the existing instruction, or only accumulate error case descriptions.
4.3 FOR WHICH DATASETS DOES PROMPT-MI EXCEL?
Per-example length. First, we perform an analysis separately over datasets with relatively short ICL examples (under 46 tokens average) and relatively long ICL examples (more than 220 tokens average). The results in Figure 4 show that PROMPT-MII benefits both short and long example datasets. However, the compression rate for longer datasets is larger, as there is more headroom to improve. We also observe that ICL scales less well for datasets with longer examples, as context length limitations become constraining.
Llama3.1-80-Instte
2wenn255-7-innsruuc
Short Exarmple Datasets (Botton 25%)
Long Example Datasets (Top 25%)
Short Examppe Datasets (Bottom 25%)
Long Example Datasets (Top 25%)
0.4%
042
034
0/3
022
Pronpt Longth (Tokezs)
Pronpt Longth (Takenn)
Prompt Langth (Tokane)
Prompt Langth (Taiann))
No testruttie
Conoett Loannng
Prrpptii Zera
Pomppry
Figure 4: Analysis of when PROMPT-MII excels over ICL by per example token length.
Analysis on Data Contamination and Similarity between Training and Evaluation Datasets. Our train/validation split is by dataset name, so there may be mix of in-domain and out-of tut-of-domain datasets in evaluation. We therefore perform both a duplication check and an embedding dataset similarity check.
Data contamination analysis. To test training-test leakage, we performed exact MDS hashing across all examples. Resulting leakage rate: 0.35% (70/19800 test examples). This confirms that the evaluation set is disjoint from the training set at the input-content level.
judge how similar two datasets are through semantic embedding cosine similarity. For each dataset, we sampled 200 input texts, computed MPNet embeddings for each, and averaged them into single dataset embedding. We then computed average kNN similarity (f- = 10) of each test dataset compared to 3000+ training datasets, binning by similarity thresholds. Results shown in Table 3
Embedding similarity
analysis. To measure generalization,

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Published as a conference paper at ICLR 2026
Across all three bins, PROMPT-MII consistently improves over PROMPT-MII-Zere (untrained) and achieves performance comparable to (or better than) 100-shot ICL.
Together, these results suggest that our method has meaningful contamination with the training set and generalizes strongly even to the most dissimilar datasets.
Performance across dataset similarity groups (measured by kNN embedding similarity). *Note that datasets with high input similarity (>x0.55 do not necessarily have the same classification task, since the embedding only based on the input text, not the labels.
Table 3:
Method
n=5
n=10
n=20
n=50
n=100
Similar Group (similarity
>
0.85, 49 datasets)
Naive
0,252
0.252
0.252
0.252
0.252
ICL
0,345
0.383
0.403
0.420
0.426
Prompt-Mil-Zero
0.313
0,326
0.339
0.351
0.332
Prompi-MIT
0.389
0.417
0.435
0.418
0.407
Moderate Group
(0.50-0.85,
39 datasets)
Naive
0,245
0.245
0.245
0.245
0.245
ICI.
0.334
0.373
0.394
0.415
0.420
Prompt-Mil-Zero
0.301
0.314
0.327
0.336
0.318
Prompt-MIl
0.376
0,402
0,418
0.406
0.396
Dissimilar Group (similarity < 0.50, 5 datasets)
Naive
0.256
0,256
0.256
0.257
0.256
ICL
0.380
0.422
0,443
0.456
0,456
Prompt-Mil-2e
0.334
0.349
0.364
0.376
0.356
Prompt-MIl
0.408
0.436
0.455
0.440
0,521
Case Analysis. In the following figure, we display some (abbreviated) example prompts to provide an intuition of where PROMPT-MII may outperforms PROMPT-MI1-Zero and ICI. for Llama3. 1-88- Instruct. All methods uses the same set of n=10 examples as input. Compared with PROMPT-MII-Zero, PROMPT-MII develops much more specific and actionable criteria. While PROMPT-MII-ZAR provides vague cues like "Useful cues include the tone and language used", FROMPT-MII provides specific guidelines on when to predict the input a certain label, with specific examples and keywords. In this case, both PROMPT-NN MII and PROMPT-MIL MII-Zeru also outperform many-shot ICL.
PROMPT- MII-Zero
PROMPT-MII
following labels: :0:2:3, The task to determine whether the inpur text is question or request for advice (label 0); a statement or opinion (label 1) spum promotional message (label
Classify input
Classify input into
following categories
based on its content and purpose:
Label This label for inputs that are asking for ad- vice: guidance; recommendations building upgrad- ing a computer; purchasing computer components; or trou- bleshooting, computer-rolated issues...
off off-topic
or unrel unrelated ated message (label 3).
Useful clues for making the decision include:
The presence of a question or request for help: which is often indicated hy words phrases such
Label 3: This label is for inputs that are unrelated to com- puter hardware software
are instead focused other
The tone and language used; which may indicate question or request for advice (e.g. polite language; uncertainty: or a sense of seeking guidance).
topies, such as business; finance; cryptocureency...
Label 2; This label is for inputs that are asking for advice guidance
computer related lopics; such educa- tion: carcer; or personal development...
- Thee content of the text: which may be related to a specific topic subject (e.g, computer hardware; medical careers; or ptocurrenncy).
-Labe] This label for inputs that do
into any of
the above categories. If an input is unelear or does not...
Respond with only label name; without any explanation additional text. Only retum ene of these options: 1; 0:
corresponding label (0; I: 2: 3) only... Only return one of these options: 1: 2; Do not output "Label:" or any extra text.
Respond
3. Do not output 'Label:' or any extra text.
Fl: 0.241
Fl: 0.829
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Published as a conference paper at ICLR 2026
In Context Learning
Input:
Not my first build but it been 10 years since built one. Have some questions. Specs B550m ds3h Ac matherboard...
Label:
Input:
Cpu cooler for 30806? ve recently purchased 30800 but my epu is 10400 Could you recommend one?
Label:
Input:
explaining and belp gaminn pe. Before ask to build one need to understand the parts and what they do; which don't know anything about so this...
just play my
broke could get
always
Label:
Only return one of these options: 1; 0; 3. Do not output "Label"" extra
FI: 0,026
4.4 CROSS-MODER TRANSFER
An advantage of Instruction Induction compared to finetuning or soft-prompt is that Instruction Induction natural language and therefore transferrable another black-box instruction follower model.
Larger Models Instruct, Smaller Models Follow We evaluate whether large models can gen- erate effective instructions for smaller cnrruction-follwiing models. Figure demonstrates that CIMMP-MMII-Zer and Qwen3-235B TROMPT-MII- MeroZero successfully generate
Llama3. 1 1-405B structions that work well with their smaller counterparts. However, surprisingly, our PROMPT-MII Llama3. 8B8B outperforms the much larger Llama3. 1-405B (Figure 5).
L 1-88--1nttB----eee
Qwen2.5-78-Instee
0.43
100
0.45
0150
0.38
n-10
0.35
0.40
0-55
0.30
0.35
0.28
0.25
0.30
2k
8k
10k
Prompt Length (Tokens)
Prompt Length (Tokens)
Neive Instruction
In-Context Learning
PromptMll-Zero
PromptiMl
Ulem33 1-4050 Qwen3-2358 InstructGen
Figure 5: Cross-model transfer results showing large model instruction generation capabilities. Pur- ple dashed lines connect larger model performance (Llama3. 1405B and Qwen3-235B) to ICL base- lines for the same number of examples, demonstrating that large models generate effective in- structions off-the-shelf.
Cross-model Transfer We investigate whether PROMPT-MII trained with one follower model can generalize different follower models at evaluation time. According our ablation results Table 6 Appendix), cross-model transfer is feasible but suboptimal compared to same-model combinations. For instance, PROMPT-MII Llama Qwen follower 1.391-0.415 115 FI) outperforms PROMPT- MII- Zero on Qwen (0.369-0.39) FI), demonstrating that training benefits partially transfer across mod- els. However, it underperforms PROMPT-MII Qwen Qwen follower (0.409-0.44) FI), revealing model-specific preferred instruction patterns. This makes intuitive sense: RL training optimizes instruction generation for the specific follower model's capabilities and preferences, learning to generate instructions that particular model responds to best. Future work can also explore larger models for instruction followers, in this work for practicality, fix the instruction follower model to smaller model, as instruction following may be applied to many test queries.
4.5 IMPORTANCE OF PROMPP TEMPLATE
The choice of meta-promp template impacts instruction generation quality, and optimal templates model-dependent. We compare two meta-prompte evaluated on both Llama3.1 1--BBBBBBBBBB..... and Qwen2.5- 7B models. We also compare against a naive meta prompt, identical to the one used (Honovich
al. 2022
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Table 4: Meta-Prompt Template Comparison: FI Performance Across Models
Llama
Qwen
Method
n=
n=100
n=50
n=100
Naive
0.253
0.253
0.303
0.303
PROMPT-MII MII- (naive)
0.287
0.272
0.343
0.360
PROMPT-MII-/ero (metal)
0.354
0.336
0.356
0.340
PROMPT-MII-Zero (meta2)
0,301
0.296
0.387
0.371
Both meta--rompp templates outperform naive instruction, but the results reveal model-dependent preferences: Llama3. performs better with metal (+0.053 Fl vs meta2), while Qwen2.5-7B achieves superior results with meta2 (+0.031 Fl metal). In this work to optimize performance, we use metal for Llama3. I-BB and meta2 for Qwen2.5-7B. Future work could explore inference- time search or automated methods select the most effective meta-prompt.
RELATED WORK
Instruction Induction Instruction induction is category of automatic prompt optimization tech- niques (APO) that takes in examples as input and induces task instruction without requiring custom hand-written seed prompt. HHnnvichh et al.] (2022) was the first to propose the problem definition of instruction induction from few-shot examples, showing that it is feasible with GPT-3 on simple tasks like "Extract the first letter of the input word" or "Sum the two given numbers", which had near-perfect ground truth instructions expressible in sentence. Our work shares similar problem definition but extending few examples to many examples, and testing on arbitrary classification tasks with ambiguous decision boundaries and often no ground truth available.
More recent methods like APE (Zhou et al., 2022) and GEPA (Agrawal et al., 2025) (and other re- lated work (Choi et al. 2025 Fernando al. 2023) cast instruction induction as evolutionary or meta-optimization problem: APE iteratively proposes and rewrites candidate prompts from exam- ples and selects the best one on validation split, while GEPA performs genetic Pareto optimization with reflective changes for LLM programs. Despite their effectiveness, these methods require exten- sive test-time search and many I.L calls, whereas PROMPT- MII produces reusable instruction in a single pass, avoiding per-task optimization at inference time.
Reinforcement Learning for Prompting Recent work applies RL to prompt optimization but optimizes prompts per target task. RLPrrompt [eng al. 2022 formulates discrete prompt op- timization as a reinforcement-leamiin policy that generates task prompts directly, often yielding non-natural ""gibbberihhuugrramaatic
outputs. PRewrite Zhang al. (2024) trains a prompt rewriter LLM with RL to take an under-optimized prompt for given downstream task and rewrite into a higher-performing prompt. PRL Batorski et al. (2025) uses RL perform instruction duction, but also trains new policy per each task. In contrast to prior RL prompt optimization work, PROMPT-MII learns a general instrectioninnductin capability that transfers to unseen tasks, eliminating per-task training at test time.
Ha et al. (203) also meta-learns an instruction induction model; however, they use supervised fine- tuning instead of RL. This requires having ground-truth instructions for many datasets, which is limited scale and difficult to obtain for arbitrary public datasets. contrast, PROMPT-MIL avoids the need for ground-truth instructions and enables training much larger collections of datasets.
Prompt Compression Prompt compression approaches can be broadly categorized as hard prompt or soft prompt. Soft prompt methods (Lester et al., 2021 Mu al. 2024 Li et al. 2024) are not human interpretable and not compatible with a black box instruction following LLM,; therefore, we omit directly comparing with them. Hard prompt methods either filter tokens, words, sentences paraphrase the text to preserve semantics more fluently
(might happen at the cost of readability), (Xiao et al. 2024). Recent work such as LLMLingua-2 Pan et al. (0024) report approximately 3-5x compression on both long-conter and short-context tasks while maintaining performance by using token pruning. In contrast, our approach transforms the semantic meaning of the prompt entirely, from dataset examples task description. This represents fundamentally different compression

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paradigm that could be combined with existing hard-prre hard-prompt compression methods for additional gains.
DISCUSSION AND FUTURE WORK
We present PROMPT-MII an automatic prompting strategy that has the advantage of 1) producing an instruction prompt that can be prefix-cached Kwon et al. (2023) and shared among all test queries 2) being optimization free at test-time, requiring only a sangle-pass inference, and 3) interpretable and compatible with black-box instruction follower model. In this paper, we show that PROMPT- MIl is effective on diverse classification tasks, which represent a common and important application for LLMs, such as LLM-as-a-judge Gu et (2025), but has future potential to extend to generative tasks as well.
One potential interpretation for why PROMPT-MII is effective is that instruction induction acts as pre-chain-of-then thothtt by analyzing relationships among examples and incorporating prior knowl- edge. Regular chain-of-thought Wei et al. (2023) is expensive because it must be performed at quest time for every query, while instruction induction front-loads this reasoning process, enabling computational savings through prefix-caching across multiple test queries.
Ultimately, the goal is to generate an instruction prompt from an entire dataset, which presents two challenging directions.
Strong long-context capability. Unlike retrieval-based long-context tasks like needle-in- a-haystack Nelson et al. (2024), we hypothesize that this task requires understanding and synthesizing the entire context in order to produce an optimal instruction output.
2. Distribution-awars iterative refinement methods. If processing entire datasets in one pass proves sub-optimal, an alternative is to only process a subset of examples at given time and iteratively merge or refine the instruction across stages. This can potentially com- plement РROMPT-MPE MII, but as hypothesized in our analysis, for classification tasks we fa- vor an iterative process that is memory-pree cenoory-rrservvng and distribution-aven -waaee,, where would continuously refine a natural language "decision boundary" that reflects the global data distribution.
Overall, our work presents a step forward in effective and efficient LLM task adaptation, and we are excited about future developments in scalable and generalizable instruction induction.
ACKNOWLEDGEMENTS
We would like to thank Omar Khattab, Valerie Chen, Jiayi Geng, Tiya Cao, Aditya Soni, Anmol Agarwal, Lintang Sutawika, and Apurva Gandhi for their feedback during the research process.
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Michael Luo, Naman Jain, Jaskirat Singh, Sijun Tan, Ameen Patel, Qingyang Wu, Alpay Ariyak, Colin Cai, Shang Zhu Tarun Venkat, Ben Athiwaratkun, Manan Roongta, Ce Zhang, Li Erran Li, Raluca Ada Popa, Koushik Sen, and Ion Stoica. Deepswe: Training state-of- the-art coding agent from scratch by scaling rl. https: DeepSWE- 2025. Notion Blog.
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A APPENDIX
A.1
RL OBJECTIVE
The training objective function is:
((0) Essss Bus. ~a00
min (rx(0) As, clip((x((), PL, 1 + PH) Ax)
(3)
where importance ratio Tx (0) is:
(i((0) To (Tx [T (((((t))
train Li))
To.. (I& (S(())
train L,))
and group-relative advantage A is:
1 n
A = R(JK, Sug L.)
Σ
R(I;, S(1l) fa))
n1
j=1
with clipping bounds PL and #и set to 0.2 and 0.4.
A.2 DATASET PROCESSING PIPELINE
Automated filtering and quality control. We obtained all publicly available text classification datasets on HuggingFace (7000+ datasets total), and used GPT-4.1-mi to automatically identify in- put and label columns by analyzing dataset metadata, column names, and example entries. Datasets with more than 50% unique labels were discarded, we are focusing classification tasks.
Training Data Processing To enhance training data diversity, for each dataset we randomly sam- ple different sets of training examples. For all datasets, we sample 30% of datasets we sample another n = 10 contexts, 20% with n = 20 contexts, and 10% with 50 contexts. This design ensured have varying number of training examples used in input prompt.
training examples. For
Evaluation Dataset Selection We started with random selection of 100 held-out datasets that already went through the automated filtering and quality control pipeline above. We then performed additional filtering. datasets dataset nipaueb/multi_ewi _eullex TomTBT/pmc_opee SetTpmmc_ppen_acceee secon_acces__x_ xml, had too long of label set, such that examples fit into context, and were filtered. Out of randomly selected 200 examples from each dataset, 3 datasets had only a single label class present and datasets had more than 100 label classes present; all 5 of these datsets were filtered. Finally, two datasets with different configs but the same labels were merged, resulting in 90 final unique datasets for evaluation.
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A.3 DETAILED TRAINING CONFIGURATION
Hyperparameters We grouped n. = 5 instructions per prompt and set the batch size to 64 prompts. The maximum context length was 4096 tokens for prompts and 1024 tokens for responses. The model was trained with a learning rate of 2 x 10-6 with a 3.3% warmup schedule for 15 epochs.
0.2, while disabling the KL penalty (use_ki_loss False) to encourage exploration and aggregating the loss with the seq-mmaa-token-meen mode. Decoding used temperature of 1.0 and top-p 1.0.
We applied asymmetric clipping (DAPO) with clip_ratio_lon
Computational Resources We used 8 HIOO GPUs per training job, with each model trained for approximately hours. Training employed Fully Sharded Data Parallelism (FSDP) with both pa- rameter and optimizer offloading, together with gradient checkpointing to optimize memory usage. To handle high concurrency (128 simultaneous requests) during batch reward computation and prefix caching, we deployed SGLang Serving for reward computation on 4 H100 GPUs, enabling efficient prefill-deoode disaggregation.
A.4 BASELINE IMPLEMENTATION DETAILS
We append identical format constraints "Only return of these options: (label names]. Do not output 'Label:' or any extra text." to the instructions for all methods, including APE and GEPA. Without explicit constraints, responses occasionally include explanation or is invalid, which hinders reliable scoring and prompt selection.
Automatic Prompt Engineer (APE). We evaluated APE using both its default meta mnea-ppommp and a custom meta-pront prompt derived from PROMPT-MII. Our setup followed the instruction induction experiments Zhou et al.] (002)), using the same hyperparameters. For each n, the n training examples were split evenly into a prompt-generation set and an evaluation set. While initial exper- iments used accuracy as the selection metric, we found that using Fl score yielded higher final scores on the test subset.
GEPA (Geneiic-Paeton We split the n training examples into training and validation sets in 1:2 ratio, following the procedure in the original paper for most datasets. We implemented Classification Adapter based on the default GEPA adapter, with only minor modifications to the language model invocation logic. All other hyperparameters were kept at their default values, with max_metric_calls set to 150. The seed prompt was initialized with our naive instruction prompt.
Baseline APE APE_META
F1 (n=50)
F1 (n=100)
0.358
0.356
0.353
0.384
Table 5: Fl score comparison of APE using different meta-prompt. META uses PROMPT- MIl's template, while APE uses original template.
A.5 ADDITIONAL RESULTS
Figures and tables in the appendix provide additional results: Figure 6 shows the RL training curve; Figure 7 illustrates Fl performance trends across different values of n: [Table reports Fi scores for different nı and Figure presents win-rate matrices comparing different baselines and PROMPT- MII.
Efficiency Analysis PROMPT-MII-Zero only requires a single LLM call to produce the prompt. This one-shot approach minimizes computational cost and is particularly suitable when resources are limited.
In contrast, the GEPA optimization framework is more compute-intente intensivieee To generate prompt, it takes max_metric_calls to evaluate all candidate prompts on minibatches and selected candi- dates full validation set. Additionally, generating new candidate instruction through reflection
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Training Curves: Validation Reward vs Steps
0.44
Validation Reward
0.42
0.40
0.38
Cwen2.5- Training
Llama3.1-88 Training
Training Steps
Figure 6: RL training curves of validation reward progression for Qwen2.5-7B and Llama3. 1--B..
Llama3:1-80- 1-8--nnruuct
Sween.5-788-nntten
0.43
0.43
500e
0.35
0.58
0.30
0.28
0.35
50
:20
100
Number of Examples (n)
Number of Examples (n)
1ime3.1-4055 235
Figure 7: Fl performance trends across different values of The plots show how each method's performance changes the number of training examples increases from 100. Lines connect the same methods across different n values to highlight performance trends. Notably, Qwen3-235B PROMPT- M-M-MIlleeee shows the best scalability as n increase.
also requires an LLM call. A higher max_metric_calls allows GEPA explore more candidate prompts but requires greater computational resources, which is a core trade-off between efficiency and performance in the GEPA framework. Therefore, our setting, GEPA typically requires least 150 LLM calls, while PRoMPT-MI--Zero only requires one and consistently outperforms.
The APE framework is more demanding. In our setting, APE generates multiple candidate prompts by making 3 subsamples and producing 30 prompts per subsample, resulting in 90 LLM calls for prompt generation. Each of these 90 prompts is then evaluated on 20 examples, requiring 1800 additional LLM calls for evaluation. Hence, the total number of LLM calls for APE is approximately 2000 per run. This makes APE substantially more expensive than both GEPA and PROMPT-MIL Zero.
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Table 6: Fl Performance across different values of indicates significance between ICL and
PROMPT-MII (Wilcoxon signed-rank test). All models are Instruct models instead of Base models
-lama3.1-8B-nstruue
Method
n=5
n=10
n=20
n=50
n=100
Naive
0.253
0.253
0.253
0.253
0.253
ICL
0.347
0.385
0,406
0.424
0.430
PROMPT-MII-Zero
0,316
0.329
0.343
0.354
0.336
PROMPT-MI (Llama3.1-4055)
0.345
0.352
0.381
0.361
0.370
PROMPT-MII
0.388
0.415
0.433
0.416
0,405
PROMPT-MII (Qwen2.5-7B)
0.342
0.358
0.353
0.347
0.311
APE
0.278
0.288
GEPA
0.296
0.299
Qwen2.5-7B-Instruut
Method
n=5
n=10
n=20
n=50
n=100
Naive
0.303
0.303
0.303
0.303
0.303
ICL
0.363
0.403
0.431
0.463
0.482
PROMPT-MII-Zert
0.369
0.390
0.383
0.387
0.371
PROMPT-MII-Zero (Qwen3-235B)
0.386
0,408
0.404
0.461
0,465
PROMPT-MII
0.409
0.434
0.441
0.432*
0,424***
PROMPT-MII (Llama3. 1-8BB)
0.391
0.412
0.438
0.434
0.415
APE
0.358
0.356
GEPA
0.346
0.347
Llama3.1-8B-instruee
Dwen2.5-78-Instruue
100
Nolve
5.6
12.2
11.1
10.0
8.9
15.6
10.0
5.6
30
In-oomeot
93.3
64.4
58.9
45.6
In-Contece Learning
90.0
00.7
05.6
50.0
PN MoRe U1A
50
86
33.3
47.8
21.1
Prampthi-Zeer
81.1
31.1
31.1
25.6
Liamad 1-455-455
Owen 3-2388
Prompe011
Instruttion
Mompesdti 201o
Anntruntion
Cerkortt
Promuttell. Ztro
Dewecl-3210 Inat Mentrnniian
Phonspoiil
Figure 8: Win rate matrices showing pairwise comparison results between different methods. Each cell (i,j) represents the percentage of datasets where method i outperforms method j. Higher values indicate superior performance across the evaluation datasets. For Llama 3.1 8B, PROMPT- MII shows a hight winrate of 52.2% compared to ICL 45,6%
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A.6
PROMPT EXAMPLES & CASE STUDY
Llama Meta-Prompt Template
You helping create prompt for language model to classify text inputs. The model should choose one label from the following options: nambes)_amms....
Here are some example inputs and their correct labels: (examples)
Write an instruction that:
Describes the classification task in way that generalizes to new inputs.
Points out any useful clues or strategies for making the decision.
Clearly tells the model respond with only the label name, and not to include any explanation or additional text.
Provide only the instruction, not the examples or labels.
PROMPT-MIL Zero for Reddit Attribution Task (yyu/reddit- attprddopttttttrrtppp
Classify the input text as one of the following labels: 1; 0: 2;
The task is to determine whether the input text is question or request for advice (label 0); statement or opinion (label 1); a spam or promotional message (label 2); or an off-topic or unrelated message (label 3).
Useful clues for making the decision include:
The presence of question or request for help: which often indicated by words or phrases such as 'I need'; "Can you'; 'How do I'; or 'What is'.
The tone and language used; which may indicate a question or request for advice (e.g. polite language; uncertainty;
of seeking guidance).
The content of the text; which may be related to specific topic or subject (e.g. computer hardware: medical careers; or cryptocurrency).
Respond with only the label name; without any explanation or additional text. Only return one of these options: 1; 0; 2; 3. Do not output 'Label:' or any extra text.
F1: 0.241
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PROMPT-MIL for Reddit Attribution Task (yyurrdddttaattrrronnpp))
Classify each input into one of the following categories based on its content and purpose:
Label 0: This label is for inputs that are asking for advice; guidance; or recommendations on building upgrading computer; purchasing computer components; or troubleshooting computer-related issues. It also includes inputs that are seeking help with understanding computer-related concepts are looking for opinions computer-related matters, A clue hardware or software.
to label is the presence of words like 'build;" 'upgrade;" "advice;" 'recommendation; "help;" 'opinion' in the input. It also often involves discussions about specific computer
Label 3: This label is for inputs that are unrelated to computer hardware or software and are instead focused on other topies; such as business; finance; or eryptocurrency. clue to label is the presence of words like 'sell;" "products;" "services;" "currency;" or 'prestige' in the input.
Label 2: This label is for inputs that are asking for advice or guidance on non-computer related topics; such as education; career; personal development. A clue to label is the presence of words like 'school;" "career," "advice;" or "paramedic' in the input.
Label I: This label for inputs that do not fit into any of the above categories. If an in- put is unclear does not contain any of the clues mentioned above; it should be labeled as 1.
Respond with the corresponding label (0; I; or only; without any explanation addi- tional text. Do not include any analysis or summary of the input in your response. Simply choose the label that best fits the content and purpose of the input. Only return one of these options: 1; 0; 2; 3. Do not output 'Label:" or any extra text.
F1: 0.829
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Published as a conference paper at ICLR 2026
ICL for Reddit Attribution Task syyu/reddi--tttrroommy
Input: Not my first build but it s been 10 years since built one. Have some questions. Specs B550m ds3h Ac motherboard Amd Ryzen 5 3600 ITBB WD blue sn550 hard drive (first time ever using one of these) 32 mb ram 800w power supply Rtx 3060 12gb graphics Plus a dvd ed So my questions are this. Do I have have the updated flash to the bios to get the pe turned on and running? didn t make a boot disk; and bought a new copy of windows. Not sure if you need boot disks anymore not or if we can just boot directly off the CD? I also intended to use my old ASUS case and install all in there but the front panel cables are not marked and they using 4 and 20 pin cables and have have a new case and power supply coming tomorrow. Am I missing anything? Like my title said I haven built my own pe like this in many years. I think had use an old floppy to boot up windows.. if that gives you an idea lol Thanks in advanced
idea where any of that goes. I
Label:
Input: Cpu and cooler for 3080ti? I ve recently purchased 3080ti but my current cpu is i5 10400 Could you recommend one? Thanks!
Label: 0
Input: need serious explaining and help use to just play on my PS4; then broke and I could get fixed but I've always wanted gaming pc. Before ask to build one I need to understand the parts and what they do; which don't 't know anything about so this is why I'm making this post. Is it really cheaper then buying prebuilt; what good parts are in my price range which isn't that large?
Label: O
Input: Need advice on a pe for my baby brother (and myself) ) Hello; everyone! Hope you all are safe and well!! need advice on this build composed for my baby brother. I was planning to buy him PS5 but wasn't 't able to get it; and naturally I thought it was good time get a PC that both of us can use. Ever since was a little girl: dreamed of getting computer exclusively for gaming. never had the funds for it before (or the time since) so never happened. I'm hoping I can play all the games never got to play with this build. My 12 brother will be playing games like Minecraft: Genshin Impact; Terraria; Among Us and I plan on playing some CS GO; Hearthstone; Portal 2: Detroit Become Human and bunch of indie games I bought on Steam. I'm mainly looking for PC that can handle 1080p gaming comfortably. I live in the UAE so buying online from Newegg; Amazon US is really
since I' I'm forced to pay shipping costs up 200 300. The parts I' m gonna buy are mostly from local merchants and a few can be ordered online (from local websites). I'm mainly looking for critique or advice. I've checked for the compatibility but just want to make sure that all the components work well together for the games that will be played. PCPartPicker Part List CPU AMD Ryzen 3600X 3.8 GHz Core Processor Motherboard MSI B550 A PRO ATX AM4 Motherboard Memory G.Skill Ripjaws V 16 GB (2 8 GB) DDR4 3600 CL16 Memory Storage Crucial P1 500 GB M.2 2280 NVME Solid State Drive Video Card Asus GeForce GTX 1660 SUPER 6 GB STRIX GAMING OC Video Card Case MSI MPG Sekira 100R ATX Mid Tower Case Power Supply Thermaltake Smart 650 W 80 Bronze Certified ATX Power Supply Operating System Microsoft Windows 10 Home OEM 64 bit If you't 'e read this far; thank you so much! Have a good day)
Label:
Input: Something that will help Doge If you sell goods; products or services; Make some available exclusively for Doge transactions. This will continue to solidify the Coin currency as well as something exclusively and filled with prestige.
Label:
Input: Which one should I go with? Idk if should go with my first choice or my second one. There isn't much difference but I still don't know which one I should go with. Any help is appreciated. Choice 1 Choice
Label:
Input: I Need Better Storage for my Legion y7000 Yes; it's gaming laptop: sue me. But I love it and so far it's played most games without issue. But the issue I've had as
Fl: 0.026
19

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Published as a conference paper at ICLR 2026
PROMPT-MIL Zero for Brazilian Court Decisions (foelmiklauss/razzllann court decisions)
Classify the given text as one of the following: no, partial, yes.
The task involves determining the outcome of a legal appeal or review.
Useful clues for making the decision include:
The presence of words like "conhecido" (known), "provido" (granted), or "dene- gada" (denied), which often indicate the outcome of the appeal.
The use of phrases like "em parte" (in part) or "parcialmente procedente" (partially granted), which suggest partial outcome.
The overall tone and language used in the text, which may convey a sense of approval, denial, or partial acceptance.
Respond with only the label name, without any additional text or explanation. Only return one of these options: no, partial, yes. Do not output "Label:" any extra text.
Fl: 0.147
PROMPT-MII for Brazilian Court Decisions (joelniklaus/braziliaa court court_decisions'
Classify each input 'yes,' 'partial,' 'no' based the model's analysis of the text. To make this determination, pay close attention to the presence and wording of phrases such "CONHECIDO E PROVIDO," "CONHECIDO E NĀO PROVIDO," "CONHECIDO, EM PARTE, E PARCIALMENTE PROVIDO, and "DENEGADA.
'CONHECIDO E PROVIDO" typically indicates "yes" label, as it implies the re- quest was fully granted.
'CONHECIDO E NÃO PROVIDO' typically indicates 'no' label, it implies the request was fully denied.
"CONHECIDO, EM PARTE, E PARCIALMENTE PROVIDO' typically indicates 'partial' label, as it implies the request was granted but not fully.
''DENEGADA' typically indicates 'no" label, it directly translates to 'denied.'
the input does not contain any of these phrases, look for other indicators such as 'revisão criminal parcialmente procedente," which suggests "partial' label.
Respond with only the corresponding label Cyess'' 'partial,' 'no') for each input, without any explanation or additional text. Do not include any analysis or context; simply
choose the most accurate label based on the input. Only return one of these options: no, partial, yes. Do not output "Label:" any extra text.
F1: 0.937
20

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Published as a conference paper at ICLR 2026
ICL for Stereotype Detection (McGill-NLP/sterel
Input: love my BLANK grandfather.
Label:
gender
Input: The Iranian had a BLANK in his personal affects.
Label: race
Input: Most prisoners are BLANK people
Label: profession
Input: People from Afghanistan are BLANK.
Label: race
Input: Jordan know for its BLANK.
Label: race
Now classify the next input. Only return one of these options: religion, profession, gender, race. Do not output "Label:" or any extra text.
Fl: 0.270
PROMPT-MII for Stereotype Detection MMGGill-NLP/streeenn
Classify each input as belonging to one of the following categories: religion, profession, gender, race. To make this decision, look for contextual clues such the type of noun that fits into the blank (e.g. "grandfather' implies gender, 'prisoners' implies profession, 'Iranian' 'Afghanistan' implies race, and 'BLANK in his personal affects' implies characteristic associated with a person's 's cultural or racial background).
Key indicators for each category:
* Gender**; References family relationships (grandfather, grandmother, son, daugh-
ter), gendered pronouns, or gender-specific roles
** * References countries, nationalities. ethnic groups. or cultural backgrounds (Iranian, Afghanistan, Jordan, etc.)
**Profession ** References to job-related contexts, workplace situations, or occupational groups (prisoners, workers, etc.)
**Reigion**** *+; References to religious practices, beliefs, institutions, or religious groups
When encountering a sentence with a BLANK, consider what type of word would logically complete the sentence and which category that word would most likely belong to. Focus on the subject and context of the sentence to determine the most appropriate classification.
Respond with only the category name (religion, profession, gender, race), without any explanation or additional text. Only return one of these options: religion, profession, gender, race. Do output "Label:" or any extra text.
F1: 0.930
21

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Published as a conference paper at ICLR 2026
Table Evaluation Datasets: Number of Labels, and Avg Tokens per Example
Dataset
FLbbels
Token Length
Millyway-isanndrria Huniian www.geennndddeeeem mwooic. revices
310
turkish wnkisd-alp-nnii/SSmmTT.e
109
116
buraskkkatylteew levical
762
AAU-NIPMEEF--PI
155
pelevilpolesa22011 cyhorbullying
DGurgunu//eengli: 30
101
MKKF-KMTThut speech slovak
MELA
peoeinglmimmttal binding
170
Mange-VanuHHidi---ae Hindi--Mrraaai-SSnaayye
sxparbpprekhsss Conprreckhhs//Ccceeeeeeeee Shesttt
jusplusohuur detection.
40
gemman/Tab-MAA
1199
hamdes///77HHS
AHPrstiaMMtllllninnni ESMFold
168
P-Pl---Dpphh-4
upnoal/mddex 700k
FangeenGuanliann wikipedia wikinews, andi
km-oofand//mmll..c AMA
118
ragaral/aactual consistency evaluation benchmark
718
mabdin 70hmerged bigvol. primeval
397
DGarguraulyeuha
OCRUCOVID d-diinformaaionn
clec/clue
37
Neseeescccc/NusaX senti LexC Gen
Morit preer/mliillggaal ML.1-36ang-2mill
CZL Clesdd sentiment balanced
3
116
sieeer reviews Jubel nnuirnn wankk/meeline
1dd
curdifiallyweet senciment multilinuual
3
Samumdersso//Nrrdiee Familjebok-Can Category Chosification Dataset
3
87
celnkiusshrrziin poc_urrdddddidonnss
abstract
PrashGaaeshCrahh Predictions2
3
2275
DusemLLClaDeest
218
TricosusssCAPPP
concpinetSenneegaatt
isck aidaabboru tags 2016 2023
3
104
odcckinteallaii2222
yyu'ediii alirprompt
181
sdadaasippe
27
WwebNoosClassificuue
NLPisteroset
RussianNLPPaap
0
263
coppitidCoppus
564
Vhussa sopies
ai fureverheaallnee classffiation
CCRU/Lamal ens ens-Englin
5D
stombergalphorddc
samimsalleems wwwprrrrumtueeeeoioirienooo multiliguul
mttV/aasalaheees
841
silvespeu/essay
713
OCRULLlamaLeem Arabic
574
mtcl/NusaP unggrrirtopuuaassttuaaa
209
dadashondeeNPessin Cooking
22
ouis, Vuitoo. Product,oocccce
Dunlonickyuhheii -albeeti-cliaatee
datases owen
ddvwiise legislation
3985
chenttngpaggcgpteskkka dansset Www..//eeoWootbtoooaoea WWN.:
122
meN/PaenCCassihhnion
iamppanc/bnkknng
10
10
chensinnpaagespteekki
660618
Lugjictte//lgggee-net
cheemlaffaanaahhh2200000
200
semurdagiddc-nneesscoo
22
505
lars las//9//trry benchmark
mickNT PTwiitrrnnnasssShheeetiii
synopa/redditt datnset
20
130
MiissOO/FrreMMMMMMCCO
111
theiden/us-coeeeeee wewpaas---ooersssss---liiieeee
mich/amzzon massive intest
tel unbsorrc/hhghench
39
196
40
AmizauSciencsiaane
clariylibbueed
ukkibdulikkill-mee please-dataset plecee-ilee
205
leopPaolaveurkes
Semeen//SeumiiTTT -ww.Sennn-------Ppeeee TTP-Mupping
122
133
PNI PHuifPrsttsttee
254
458
evalithfiisaiity recageition
22
lmmorhhillnnsdi catsel 84
493
179
503
595


Reviews:
[{"review_id": "Vy2LB8SGxA", "reviewer": "Reviewer_9dwF", "summary": "This paper addresses the challenge of high inference cost in in-context learning, where long context sequences are often required to achieve strong performance on new tasks. To mitigate this issue, the authors propose compressing training demonstrations into a more compact yet informative prompt. They introduce PROMPT-MII, an RL-based framework for meta-learning an instruction induction model capable of generating such prompts. Experimental results on several classification benchmarks demonstrate the effectiveness of the proposed approach.", "questions": "1. How does the proposed method perform on more complex tasks, such as open-ended question answering or reasoning-based generation tasks?\n\n2. When applying the approach to a new domain or task, how should the meta-prompt template be constructed or adapted? More discussion or guidance on this would be helpful.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The problem of reducing context length in in-context learning is both timely and important, given the growing reliance on in-context learning for various downstream tasks.\n\n2. The method is evaluated on multiple classification tasks, and the results indicate clear improvements over baselines.", "weaknesses": "1. The experiments are limited to classification tasks, which makes it difficult to assess the generalizability of the approach. Additional evaluation on generative tasks, such as question answering, would strengthen the contribution.\n\n2. It is unclear how robust the meta-prompt design is across different tasks. The example prompts appear to be tailored to specific classification tasks, raising concerns about adaptability and generality.", "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": "SBSwPSFMxg", "reviewer": "Reviewer_dUHf", "summary": "LMs has ability to generate better instruction to improve downstream performance. This work proposes to meta-learn this capability across variety of classification datasets, using RL.", "questions": "### Questions\n\n1. Could authors discuss this work in relation with below prior works? These works seem to study meta-learning instruction generation. I am open that this work has unique contribution w.r.t. below works, but it will benefit the readers to know how this work differs.\n    1. Choi ‘25, System Prompt Optimization with Meta-Learning\n    2. Ha ‘23, Meta-Learning of Prompt Generation for Lightweight Prompt Engineering\n    on Language-Model-as-a-Service, Findings of EMNLP\n    3. Fernando ‘23, Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution (I see this in bibliography, but cannot find in the main text or appendix)\n2. (L250-L253) Could authors elaborate more on why “classification tasks may be challenging for iterative refinement algorithms”, compared to \"generative tasks like QA or summarization”? Further explanation will help readers to understand the unique benefit of meta-learning instruction induction over existing non-meta-learning algorithms\n3. I have confusion about the naming of Prompt-MII-Zero. It seems to be the “untrained” version of Prompt-MII, which means that simply the LM is prompted to generate an instruction given meta-prompt. Prior works [1, 2] have also shown efficacy of generating instruction using LM, which seems to be largely identical to “Prompt-MII-Zero”. I believe more straightforward naming, or at least more explicit explanation can reduce the confusion of the readers, especially those who are skimming through.\n4. (Minor details) L248 only mentions Qwen, but shouldn’t it also include Llama? Or does that explanation only holds for Qwen for some reason? If so, authors should elaborate on that.\n\n[1] Honovich ‘22, Instruction Induction: From Few Examples to Natural Language Task Descriptions\n[2] Zhou ‘22, Large Language Models Are Human-Level Prompt Engineers", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Strong motivation. Meta-learning instruction induction to generate optimized instruction for unseen task (instead of direct optimization on it) looks valid and promising direction.\n- Experiments are generally well-executed, to test the effectiveness of the method.\n- Experiment results support the strength of the method. Prompt-MII vastly outperforms naive instruction, and also is better than the untrained version(”Prompt-MII-Zero”). Prompt-MII matches the performance of ICL with 20-100 demonstrations.", "weaknesses": "Authors emphasize meta-learning as a key strength, where trained model can generate better instructions in unseen tasks. However, experiments on OOD generalization is not explicitly conducted. 90 held-out datasets, even though they are not seen during training, may be (or not be) in-distribution to the training set. Additional experiments on OOD generalization, with more explicit control on the domain of test set, will provide more insights on the authors’ proposed method.", "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": "6SLiZFwRRq", "reviewer": "Reviewer_Fcgk", "summary": "This paper trains a language model to generate an instruction given a couple of example input-output pairs of a task via reinforcement learning.  The experiments show that this method achieves the performance of ICL with >20 examples, while consuming much fewer tokens for inference.", "questions": "1. How do you prevent data contamination? That is, how do you make sure that the training dataset does not contain any questions from the holdout test set? \n2. A small issue: Line 125, the hyperlink is wrong.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The idea is clearly conveyed.\n2. I believe it is the first to use RL to train a general model for instruction induction.\n3. The experiment is large-scale, and thus the resulting model might be useful to the community.", "weaknesses": "1. The technical contribution / novelty is limited. \n2. Missing comparison with a naive meta-prompt.", "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)