Motion-R1: Enhancing Motion Generation with Decomposed Chain-of-Thought and RL Binding
Authors: Runqi Ouyang
Haoyun Li
Zhenyuan Zhang
Xiaofeng Wang
Zeyu Zhang
Zheng Zhu
Guan Huang
Sirui Han
Xingang Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Human Motion Generation
Chain-of-Thought
Reinforcement Learning
Primary area: generative models

Abstract: Text-to-Motion generation has become a fundamental task in human-machine interaction, enabling the synthesis of realistic human motions from natural language descriptions. Although recent advances in large language models and reinforcement learning have contributed to high-quality motion generation, two major challenges remain. Existing approaches often fail to capture the temporal and causal complexities inherent in natural language, leading to oversimplified or incoherent motions. Additionally, RL-based methods are frequently overly complex, hindering their scalability and adaptability across various motion generation tasks.
    To address these challenges, we propose **Motion-R1**, a novel framework that combines decomposed Chain-of-Thought reasoning with reinforcement learning to enhance both the quality and interpretability of generated motions. Specifically, we introduce the **Decomposed CoT Data Engine**, which leverages an automated pipeline to synthesize high-quality reasoning data, allowing the model to better capture the temporal dependencies and causal relationships of human motion. We also propose **RL Binding**, a reinforcement learning strategy that incorporates multi-modal text-motion alignment into the RL reward function, guiding the model to produce motions that are both semantically accurate and motionally realistic. Extensive experiments across benchmark datasets demonstrate that Motion-R1 achieves state-of-the-art performance, with a 3.5\% improvement in MM-Dist on HumanML3D and improvements in R-Precision and FID on KIT-ML and BABEL, surpassing existing methods across key metrics and highlighting its superior capability in handling complex motion generation tasks.

Published as a conference paper at ICLR 2026
MOTION-RI: ENHANCING MOTION GENERATION WITH DECOMPOSED
AND RL BINDING
Runqi Ouyang!.2.3 Haoyun Li Zhenyuan Zhang4.5x Xiaofeng Wang'
Zeyu Zhang* Zheng Zhu Guan Huang" Sirui Han Xingang Wang
Institute of Automation, Chinese Academy of Sciences
School of Artificial Intelligence, University of Chinese Academy of Sciences LLoyang Institute for Robot and Intelligent Equipment
*GigaAI HHong Kong University of Science and Technology
ABSTRACT
Texi-to-Motion generation has become a fundamental task maaiann-macineeee in- teraction, enabling the synthesis of realistic human motions from natural language descriptions. Although recent advances in large language models and reinforce- ment learning have contributed high-quality motion generation, two major chal- lenges remain. Existing approaches often fail to capture the temporal and causal complexities inherent in natural language, leading oversimplified or incoherent motions. Additionally, RL-based methods are frequently overly complex, hin- dering their scalability and adaptability across various motion generation tasks. To address these challenges, we propose Motion-R1, a novel framework that combines decomposed
of-in-oo-Though reasoning with reinforcement learning to enhance both the quality and interpretability of generated motions. Specifi- cally, we introduce the Decomposed CoT Data Engine, which leverages an au- tomated pipeline to synthesize high-quality reasoning data, allowing the model to better capture the temporal dependencies and causal relationships of human mo- tion. We also propose RL Binding, reinforcement learning strategy that incorpo- rates multi-modal (ext-motion alignment into the RL reward function, guiding the model to produce motions that both semantically accurate and motionally real- istic. Extensive experiments across benchmark datasets demonstrate that Motion- RI achieves sfaeeofftthearrt performance, with a 3.5% improvement in MM-Dist on HumanML.3 and improvements in R-Precision and FID on KIT-ML and BA- BEL, surpassing existing methods across key metrics and highlighting its superior capability in handling complex motion generation tasks.
INTRODUCTION
Text-to-Motion (T2M) generation Guo al. (2022c); Tevet al. (2023): ; Guo al.) (2022a) has emerged as fundamental task in numan-maching interaction, enabling the synthesis of realistic human motions from natural language descriptions. Driven by rapid advancements
large lan- guage models (LL.Ms) Achiam et al. (202)): Li et al. (2023), recent T2M approaches [Jiang et al.) (0023): Wang et al. ;004);:V Wu et al. (2024) have made significant strides in generating high-fidelity motions that align with complex textual instructions. Reinforcement learning (RL) Kaelbling et al. (1996) provides a promising approach to enhance motion generation by optimizing it for motion quality. Recent works Liu al. (2024); Haoru Wang al. (2025) have successfully integrated RL into motion generation, improving both text adherence and motion quality by aligning with human perceptual preferences. However, despite significant advancements in motion generation, current methods still face two major challenges.
"Equal contribution.
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(1) Existing approaches predominantly rely end-to-end supervised learning Ahn al. 2011 7);
Hong et al. (2022); Abuja & (oorency((019)) directly mapping textual inputs to motion sequences. While this approach is simple, it fails capture the deeper temporal and causal relationships inher- ent in natural language. For instance, a high-level task like "making a cup of coffee" involves a series of connected sub-actions (e.g., reaching, grasping, pouring, stirring, placing), which require care- ful temporal ordering and causal reasoning. However, these methods often struggle to effectively decompose such tasks, resulting in oversimplified incoherent motion generation.
(2) RL-based methods, such as MotionRL Liu et al. (2024) and MotionCritic Haoru Wang et al. (2025) demonstrate improvements in motion quality but are often overly complex and over- engineered. These designs, though effective, limit their adaptability to real-world applications. The intricate nature of these RL models makes them difficult to scale and deploy across a wide range of motion generation tasks, especially when simplicity and efficiency are needed.
Human
(+) Poor Generolization in Traditional T2M
(c) Expensive Reword
Annotation
train |
(Caption)
Generative Model
Motion
Pred. Motion
Preference Model
RL Reward
x
(b) Strong Generalization with Decomposed CoT
(d) Efficient Aligrment
Decomposed
GT Motion
Generative Model
Co
Pred.
(Caption)
Motion
Binding
Motion
(Caption)
Figure 1: Comparison of traditional approaches and our Motion-RI framework. (a) Traditional end-to-end models exhibit poor generalization on
motions. (b) Our Decomposed CoT Data Engine enables strong generalization by structuring high-level instructions into interme- diate reasoning steps. (c) Existing RL-based methods rely on expensive human annotations to train preference models for reward signals. (d) Our RL Binding mechanism achieves efficient multi- modal alignment without additional annotation cost.
Our motivation is to advance motion generation by introducing a novel framework, Motion-R1 that effectively addresses the key challenges in the field, as shown in Figure
To tackle the first challenge, we propose Decomposed CoT Data Engine which synthesizes high- quality, step-by-step reasoning data. Inspired by the recent success of Mhain-of-Thougee (CoT) Wei al. 2022) prompting in enhancing reasoning capabilities of L.LMs, we hypothesize that explicitly modeling intermediate reasoning steps can similarly benefit motion generation tasks. By decom- posing high-level instructions into structured action plans, our engine leverages an automated CoT annotation pipeline that employs LLMs to generate structured motion planning paths, enabling mod- els to better capture temporal dependencies, causal relationships, and fine-grainn ane-ggrined semantic nuances embedded in language. This approach not only enhances the realism and coherence of the gener- ated motions but also reduces reliance on expensive manual annotation, making the process more efficient and scalable.
To address the second challenge, we introduce RL Binding, a novel reinforcement learning strat- egy that enhances motion generation by incorporating multi-modal alignment mechanism within the RL framework. RL Binding simultancously aligns the generated motion sequences with both the ground-truth motions and the corresponding textual descriptions. This dual alignment process ensures that the generated motions not only stay temporally consistent with real-world actions but also faithfully capture the semantic meaning conveyed in the textual instructions. By embedding these alignments directly into the RL reward function (specifically, the motion similarity reward, the semantic similarity reward, and the format reward), RI. Binding guides the model to produce mo- tions that are both semantically accurate and motionally realistic. This streamlined design simplifies the optimization process, improving both the quality and interpretability of the generated motions, while offering highly adaptable and efficient solution for real-world applications.
Moreover, to demonstrate the effectiveness of our approach, we conduct extensive experiments on multiple benchmark datasets. Our method achieves 3.5% improvement in MM-Dist on Hu- manML3D Guo et al.] (2022a). while also setting new ffate-of-the-ar records in R-Precision and FID both KIT-MI. Plappert al. (2016) and BABEL Punnakkal et al. 2021). These results
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highlight the superiority of our method in both motion quality and task performance, showcasing its strong potential for real-world applications.
In general, our work's contributions be summarized in the following three folds:
Decomposed CoT Data Engine: We introduce a novel framework for synthesizing high-quality, step-by-step reasoning data. By leveraging automated CoT annotation pipeline powered by LLMs, our approach effectively captures the temporal dependencies and causal relationships in- herent in motion generation. This method significantly improves the quality of generated motions while reducing the reliance on costly manual annotation, making the process more efficient and scalable.
RL Binding Strategy: We propose RL Binding, a novel reinforcement learning strategy that integrates multi-modal text-motion alignment into the reward function via the motion similarity reward, the semantic similarity reward, and the format reward, effectively guiding the model to generate motions that better adhere to textual instructions while maintaining high motion quality. The simplicity and effectiveness of RL Binding enhance the model's 's ability to optimize across both modalities, resulting in improved precision and interpretability in motion generation.
Comprehensive Experimental Validation: We conduct extensive experiments across multiple benchmark datasets, demonstrating the effectiveness of Motion-R1. On H3manMI.3D, achieves a 3.5% improvement in MM-Dist, with Diversity and R-Precision metrics reaching state-of-the- art or on-par performance. On KIT-ML, Motion-RI sets new records in R-Precision and FID, outperforming existing methods. On BABEL, it achieves state-of-the-art performance across all key metrics, including R-Precision, FID, MM-Dist, and Diversity. These results highlight the model's superior ability to generate diverse, high-fidelity, and semantically aligned motions.
2 RELATED WORK
Multimodal Large Language Models Recently, multimodal large language models (MLLMs) Wu et al.] (2023): Koh et al.] (2023); [Tng et al.] (2025) have shown impressive performance in various tasks, including text generation, reasoning. and even multimodal tasks. For example, models like GPT-4 Achiam et al. (2023) and BLIP-2 Li et al. (2023) have demonstrated the ability understand and generate text and images. While recent MLLMs have significantly improved their perception and generation capabilities across diverse modalities, many complex real-world tasks demand not only understanding but also reasoning Banerjee al. (202T); Xiong et al. (2024). Consequently, enhancing the reasoning abilities of MLLMs has become important research direction.
Building on the success of CoT Wei et al. (2022) prompting in NLP, researchers have explored extending CoT-style reasoning to multimodal settings. For instance, models like LLaVA-CoT Xu (202)) introduce a novel vision-language model capable of performing autonomous, structured, and multistage reasoning by decomposing complex questions into four stages: summary, caption, reasoning, and conclusion.
While structured reasoning enhances interpretability and controllability, recent studies Tan et al. 2025); Pan et al. (2025); Liu et al. (2025) show that it can be further optimized through reinforce- ment learning. Building on GRPO Guo et al. 2025), we introduce decomposed CoT paradigm for structured motion reasoning and an RL Binding mechanism, together enabling interpretable and semantically aligned motion synthesis.
Text-guided 3D Human Motion Generation Text-guided 3D human motion generation has be- come a key research focus recent years. Early methods Ahn et al. (2017): Ghosh al.) (2023) primarily relied generative adversarial networks Goodfellow et al. (0014 to synthesize human motion from text descriptions. These methods often struggled with issues such as limited diversity.
To address these challenges, diffusion models Ho et al. (2020) have gained popularity in this field. Notable works Zhou Wang (2022): Kim et al. (2023) Dabral et al. (2023) include MDM Tevet al. (2023), which first applies diftusion modeling to motion synthesis; MotionDiffuse Zhang al. (2022a) improves temporal consistency: MotionChain) DiaionnCainJianggaaaa et al. (224)) explores fine- fine-graine grained struc- ture and staged modeling; and MoMask Guo et al. (024)) employs masking strategies for improved
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training efficiency. However, these methods often rely on complex architectures and extensive train- ing data and are limited by their motion initialization.
In parallel, VQ- VAE-baseee methods Guo et al. (2022a); Hong al. (2022); Petrovich et al. (2022) Guo et al. (2022b); Athanasiou et al. (2022); Zhang et al. (204b; 2025b); Li et al. (2025): Zhang et al. (2024d a) discretize motion representations to facilitate efficiett sequence mode ing and better alignment with language, providing a more interpretable latent space. Building on this, LLMs have been integrated into the generation process enhance the quality and control of generated motions. For instance, T2M-GPT (Zhang et al. (2023)) combines VQ- "Q-VAE with GPT in a two-stage frame- work for high-quality generation. Subsequent works such as MotionDiffuse Zhang et al. (2022b), MotionCLIP Tevet et al. (2022), and MotionGPT Jiang et al. (2023) integrate diffusion modeling, contrastive learning, and Transformer architectures to enhance fidelity and semantic consistency fur- ther. MotionAgent Wu et al. (2024) further incorporates semantic planning for robust generalization in complex task scenarios.
As the development of reinforcement learning, several approaches have emerged to enhance text-to- motion generation. InstructMotion Mao et al. (2024) uses trial-and-error in reinforcement learning for generalizable motion generation. ATOM Han et al. (2024b) enhances the alignment between gen- erated motion and text prompts using rewards from vision- lanounlanguage models. ReinDiffuse Han et al. (224a)) applies reinforcement learning to guide diffusion for motion synthesis. RLPF Yue et al. 2025) utilizes physical feedback from simulation environments to align motion models with hu- manoid policies. M al. (2024) leverages PPO Schulman et al. (2017) to improve gen- eration based on human preferences and prior knowledge. MotionCritic Haoru Wang et al. (2025) introduces critic model align motions with textual semanties via preference optimization.
Despite recent advances, T2M methods largely rely on end-to-end mapping and lack explicit rea- soning over complex instructions Zhang et al. (2024c; 2025a). Our approach addresses this by combining a decomposed CoT mechanism for interpretable motion reasoning with an RL Binding strategy for efficient multimodal alignment, enabling coherent and semantically grounded motion generation without costly annotations.
METHOD
3.1 MOTION-RE R1 FRAMEWORK
Motion-RI comprises two core components: a pre-trained motion tokenizer and an LLM equipped with action-oriented reasoning capabilities. The motion tokenizer discretizes continuous motion sequences into motion tokens and reconstructs them back into smooth, coherent trajectories. Mcan- while, the LLM is designed perform structured reasoning enabling it to decompose complex action descriptions into finer-grained and logically ordered sub- actions, a process we term decomposed CoT reasoning. Based on this structured interpretation, the model generates high-quality motion token sequences that faithfully reflect the intended behavior.
natural language instructions,
As shown in Figure 2 our framework consists of two training stages. we employ the Decomposed CoT Data Engine, novel automated pipeline for synthesizing high-quality, step-by-step reasoning data, which enables cold-start supervised tuning of the LLM to produce puts in the <t <think>, <output>, and <Mot ion> format. By distilling the reasoning capabilities of LLMs into structured motion planning paths, this stage provides interpretable supervision and reduces reliance on costly manual annotation, enabling efficient and scalable pretraining of LLMs.
out-
In the second stage, we refine the LLM via reinforcement learning using our proposed RL Binding strategy. Building on the GRPO Shao et al. (2024) framework, RL Binding streamlines optimization by embedding multi-modal alignment directly into the reward function, jointly evaluating embedded motion similarity with ground-truth trajectories and semantic consistency with textual descriptions. Unlike prior RL-based methods that rely on costly human annotations for preference models, RL Binding efficiently guides the model generate motions that both semantically faithful and motionally coherent. This design maintains simplicity and adaptability while enhancing precision, interpretability, and overall motion quality.
By combining the Decomposed CoT Data Engine and RL Binding, Motion-PR RI effectively captures temporal and causal structure while ensuring semantic fidelity and motion realism, providing uni-
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(Caption): "A person does Tai chi."
think>I first need to understand what Tai chi entails...
<output>
stand and shift weight forward slightly.
Start
Then, consider
Nixt,, weight splecementt.
Finally, posiitioing.
rotates one arm downward.
shifts weight onto one leg. tronsferring from the other.
I need verify each step unique, non-overlopping, and accurately reconstructs the intended motion when combined.... </think>
brings one hand to chest height, palm facing upwards.
/output>
Frozen
Decomposed CoT Prompt (Caption)
let solve this step by step.
Trainable
<think> </think> coutput> </output>
CoT Tokens
SFT
LLM
Mation Tokens
Motion
Enc
<otionn>
</ </Motion>
Decomposed CoT Data Engine
GRPO with Binding
Farmat Reward
F2
Decomposed CoT Prompt (Caption)
LLM
Dec
Motion Similarity
Group Computatiin
(Caption)
Semantic
A2
A
Similarity
RL Binding
GRPO
Figure 2: Overview of the Motion-R1 framework. Our method introduces key innovations: (1) a Decomposed CoT Data Engine that generates structured motion planning traces (including <think>, <output>, and <Motion> tokens) via LLM reasoning, enabling fine-gratd ane-ggained tem- poral and causal decomposition; (2) an RL Binding mechanism with GRPO- basedd training that streamlines optimization via embedded multi-modal alignment, ensuring semantic accuracy and motion realism without human annotations.
fied framework for coherent, interpretable, and high-quality motion generation without the need for costly human annotations.
3.2 MOTION TOKENIZER
To integrate motion data, which differs significantly from natural language in structure and modality, into the LLM framework, we adopt VQ-VAE architecture as motion tokenizer. This approach has been widely adopted in Zhhang et al. (2023a); Diang al. (2023); Guo et al.) (2024); Wu et al.) 2024) and proven effective for 3D human motion data modeling.
The motion tokenizer comprises an encoder E and decoder D. Given an input motion sequence mi.7 RTXD, with 7' frames of D dimensions each, the encoder E' maps the sequence to
latent representation (1/( E xTd/ where is the temporal downsampling rate and is the number of latent dimensions. Each latent vector z; is then quantized using a learnable codebook
C {cn, JN m=11 where N is the codebook size and c, E Ed represents a discrete motion token. The quantization selects the nearest code vector to each embedding:
2 arg min ||z - C,, C
(1)
The original motion sequence is reconstructed m.1t (( Following Zhang et al. 2023a). train the VQ-VAE model using composite objective that includes a reconstruction loss Lecconstructs a codebook commitment loss Leommits and an embedding loss Lembed
Lug Lyeconstruct Lecomit
Lembed
Here, Lyconstruct includes smoothed L1 loss with velocity regularization improve generation quality. To ensure stable and efficient training, we follow Zhang et al. (2023a) to incorporate exponential moving average (EMA) updates for the codebook along with codebook reset strategies.
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Dataset caption
Complex coption-
Abstract caption- OOD
MotionLLM
Ours
After hearing loud noise, person around, stepped back coutiously with hands raised defensively, and then slowly approached.
walks in circle
serving badminton
Figure 3: Visualization comparisons with MotionI.LM Wu et al. (024) on in-distribution and
out-of-distribution prompts.
3.3 DECOMPOSED COT DATA ENGINE
We introduce the Decomposed CoT Data Engine, an automated module for generating structured reasoning traces to guide text-to-motion generation. This engine leverages the reasoning and se- mantic planning capabilities of LLMs to transform free-form motion descriptions into high-quality, step-by-step CoT action plans, providing intermediate supervision that bridges language and motion.
The engine starts by augmenting cxisting motion- motion-language datasets using prompt-based LLM queries. We design comprehensive prompts that instruct the LLM to decompose tasks into logically ordered sub-actions while respecting temporal dependencies and action semantics. These prompts include clear instructions, output format constraints, and in-context examples to ensure structured reasoning outputs. The complete details of the prompt are provided in the Appendix [A.2 material.
Generated CoT traces are then evaluated through automated quality control pipeline. We em- play DeepSeek-RI Guo et al. (2025) to assess each trace for relevance, logical consistency, and conciseness. Traces that exhibit redundancy, overthinking, verbosity are filtered and regenerated until they meet quality standards. This iterative filtering and regeneration process serves our self- verification mechanism, ensuring high-quality training data. Figure 2 iillustrate the full Motion-RI pipeline, with the Decomposed CoT Data Engine highlighted to show the structured reasoning trace generation process. For the task "A person does Tai chi", the engine identifies the main action, decomposes it into sub-actions such "stand up"" "arm movements", "weight displacement" and "hand positioning", and further elaborates each sub-action with motion-relevant details like move- ment direction and involved body parts.
Each validated CoT trace paired with its original textual description and corresponding motion se- quence, forming triplets of the form (description, decomposed CoT, motion). By distilling structured action plans, the Decomposed CoT Data Engine enables the model to capture fine-grained tempo- ral and causal dependencies directly from language, significantly improving controllability, inter- pretability, and generalization in complex motion generation, while drastically reducing reliance on costly manual annotation.
3.4 TRAInING STRATEGY
3.4.1
COLD-START TRAINING WITH DECOMPOSED CoT
Inspired by DeepSeek-R1 -eeooo Guo et al. (025), we first attempt end-to-end RI. training to induce decomposed CoT reasoning and motion generation purely from reward signals. Yet this setting proves unstable, as the model often fails to produce coherent reasoning or valid motion tokens. This instability stems from two factors: motion generation requires long, structured sequences rather than short symbolic outputs, and motion tokens are newly introduced symbols with insufficiently trained embeddings to bridge the modality gap.
To mitigate these issues, we adopt a cold-start strategy based on supervised fine-tuning. Leverag- ing curated triplets of captions, decomposed CoT' traces, and motions, we bootstrap the model's

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capacity to produce structured reasoning and valid motion outputs, providing stable foundation for subsequent RL optimization.
3.4.2 GRPO-BASED TRAINING WITH RL BINDING
To enhance alignment between textual instructions and generated motions without incurring costly human annotations, RL Binding formulates text-to-motion generation as a reinforcement learning problem, employing GRPO Shao et al. (2024) optimize the generation policy efficiently.
For each text prompt g, a group of G outputs {01, O2,..., are sampled from the old policy model Told. Each output assigned a scalar reward, yielding reward vector r {ri, r2...... computed by task-specifie reward functions that evaluate the quality of each output. GRPO then updates the policy model by maximizing the following clipped objective:
(o)
G Ec Σ
min (T(0.1l)) A. clip (o,1g))))
-1,,1 ,,1+e) A.) (oil))))))
To(o,lg)
(3)
3 DKL (eeet)
where e and are hyperparameters controlling the clipping range and KL regularization strength, respectively. Tret denotes the reference policy model. The normalized advantage term is given by A, *i-mean(x) providing normalized signal that facilitates stable and efficient policy updates. sid(rr
The reward mechanism in RL Binding focuses motion similarity and semantic alignment, ensur- ing that generated motions are temporally coherent and semantically faithful to the textual input. A format reward is also included to maintain structural validity, collectively enabling effective rein- forcement learning without costly human annotations.
Format Reward. To ensure that the content generated by the model has a resolvable structure, we introduce Format Reward Tiommat- This reward detects through regularization expressions whether the generated results strictly follow the predefined format:
<think> (Decomposed
(Motion tokens)</Motions
Here, curly braces and their enclosed content denote placeholders, representing the generated CoT and the corresponding motion tokens, respectively. If the generated content exactly matches this required format, we assign reward of 1. Otherwise, the reward set to O.
Motion Similarity Reward. To enforce temporal and spatial consistency, RL Binding computes motion similarity reward Fimotion between the generated motion i and the ground-truth motion m. A pre-trained motion encoder fmotion Guo et al. (2022a) is used to extract feature embeddings, and the reward is defined as the cosine similarity between these embeddings:
Smotion (m) fmstioon (m)
F mmotion
(4)
[l (m))ll
|l motioon m
||
Semantic Similarity Reward. To ensure that the generated motion semantically corresponds to the input textual description T, RL binding evaluates the semantic similarity reward Tsemantic which measures the alignment between the motion embedding and the language embedding in a shared latent space. Both embeddings extracted using pre-trained encoders fmotion and fiett respectively (also from Guo et al. (2022a)). ). The reward is defined as:
fmnttion (m) Fiex (T)
T' ssemantic
(5)
f m ||2 (ffea(()))
These rewards are integrated into GRPO via groupwise preference ranking, enabling RL Binding to simultaneously enforce motion realism and semantic fidelity.
4 EXPERIMENTS
4.1
EXPERIMENTAL SETTINGS
Datasets We evaluate our method on two widely-used benchmarks: HumanML3D Guo et al.
(2022a). KIT-ML Plappert et al. (2016) and Punnakkal et al. (202T). Human M.3 con-
tains 14, 616 motions and 44, 970 textual descriptions, sourced AMASS Mahmood al. 2019) and

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Table 1: Quantitative results of Motion-Ri on HumanML3D Guo al. (2022a) and KIT- ML Plappert et al. (2016). The evaluations are conducted 20 times to obtain a 95% confidence interval. Best results are highlighted in bold and the second best in underline.
Methods
R-Precision 1
FID
MM-Dist J
Diversityt MModality
Top
Top 2
Top
HumanML3D
MDM
Tevet al.
2023
0.320=-005
0.4
0.611 007
0.544
5.566
9.55g
2.799
MLD Chen ei al:
2124
0.481 1mm0
0.673-1.00
0.772l..u .m
0.473
3. 3.1964.0u
9.724
2.413 2.413 1.
MoiinDiilusee (Zhang
20224
0.491 101::.000 2.000
0.681 2..81
0.782
0.630
3.113
9.410
+.553+.002
T2M (Gio et al A122
0.4574.00
0.5594.007
0.740 0.740-..u .m
1.0674 1.067
3.340 cos
9.188 umo
2.000
TM21 Guo
0,124 003
0.618 003
0. 0.729= 000
1.5014-017
3.167 011
3.589 070
2/121 osa
T2M-GPT /hnn el sl.
M123a
0.49)
0.680
0.775
0.116 004
3. 3.118$0001
9.76 081
1.856+00:1
MotionGPT Unang el
0.492=.003
0.681#t000
0.778#.00 0012
0.232 .00s
3.096*.006
9.52
t.005
MoMask Guo el al.
2024
0.521 4.000
0.713
0
0.045
2.958 000
9.620
1.211
Motion hum Dung el
2024
0.504#.004
0.617
0.730=.000
0.248
3.0333+-000 FOLD
9.470#
1.727#.004
Motionl M
Wa cl
H1443
0.515 un
0.691 09011.0000
0.801 2:
0.230
2.9674.0u
9.008
2. 2.14
MotionGPT T-2
Wang ot al
2024
0.496 000
0.691 1o0
0.782
0.191 1..00
3. 3.0804.003
9.800
2.137
Motion-RI
0.515 L.m
0.719
0.818 002
0.201110004
10.026
075
2.317
KIT-ML
TM2T
Gup et al. 2022b
0.280 uns
0.463
0.587
3.509
4.591 t0:00
9.473
3.292
T2M
Cilo
20225
0.361 1 one
0.559
0.68
3.022
3.483 1.mzs
10,720 1:3
2.0524..107
MDM
Tsvet al 2023
0.161
0,291 nos
0.396 004
0.4197
9. 9.191 4000
10 10,850 109
1.907#.924
Motion
0.417#.004
0.6:21#t000
0.739=.000 004
1.954+0002
2 2.95
11.100 145
0.733)=.003
ML.D Chen et an 20225
0.330
0.609
0.734
0.404
3.204
10,800
2.192#
T2M-GPT [Zhang al
20230
0.416 006
0.6275t.000
0.745
0066
0.514
3.007
10.920 105
1.570
ArtT2M Zhong al.
0,113
0. #..3
0.751
0.870=+.00m
3. 3.039
10.960
123
2.281 #.047
Motionl LMM
Wu et al.
2024
0.400 0.409 me
0 0.624
0.750 mms
0.78144.00
2,982
Looz2
11,407
100
Motion-R1
0.431 + 000
0.638 002
0.761 + 000
0.287
004
3. 3.196 1410
10.875
002
2.262
KIT-ML includes 3, 911 motion clips and 6, 278 descriptions, de- rived from KIT Mandery et al. (2015) and CMU CMU Graphics Lab datasets. BABEL provides over 43 hours of motion capture sequences with 28k sequence-level and 63k frame-level action annotations from AMASS Mahmood et al. (2019).
2umanAct12 Guo al. (2020).
Evaluation Metrics Following standard protocols Guo et al.) (2022a); Zhang et al. (202aa): Wu
et al. (2024), we report R-Precision @ 1/2/3, FID, Diversity, MM-Dist, and MModality. These met- rics respectively evaluate retrieval accuracy, distributional realism, sample diversity, text-motion alignment, and one-to-many generation ability.
Implementation Details We utilize DeepSeek-RI Guo et al. (2025) to generate structured reasoning traces for complex motion descriptions, serving as intermediate representations that bridge textual prompts and motion tokens. Within Motion-R1, we adopt Qwen- 2.5- B--B-IInntrude Team (2024) as the backbone model for its strong multi-step reasoning and efficient size, facilitating stable RL training. The generated CoT's condition motion synthesis and provide targets for GRPO optimization. Ex- periments are conducted on NVIDIA H20 GPUs. Additional experimental details provided in Appendix A.3
4.2 QUANTITATIVE EVALUATION
We evaluate the performance of Motion-RI R1 on HumanML3D and KIT-ML datasets, comparing it with sate-oofthe-ee methods, ranging from VAE-based to diffusion-based models. For the diffusion- based model, we select MDM Tevet et al. (2023), MLD Chen et al. (2023), MotionDiffuse ZZhag eet al. (2022a). For the VAE-based model, we choose T2M Guo et al. (2022a), TM2T Guo et al. (2022b), T2M-GPT/Zhan et al. (0023a). MotionGPTTjang et al. 2023). ) MoMask Guo et al. (024)) MotionChain Jiang et al. (2024), L. (024)) and MotionGPT-2 Wang et al. 2024
We follow the same evaluation settings prior work Guo et al. (2022a): Wu et al. (2024) and the BABEL setup from|Zhuo et al. (2024). Quantitative results on HumanML3D and KIT-ML are shown in Table I Results on BABEL provided in the Appendix A.4
On HumanML3D, Motion-RI attains strong and balanced performance across semantic and motion-quality metrics. It achieves R-Precision @ 1/2/3 0.515 / 0.719 / 0.818, with the latter two values being the best in the table and R-Precision at near-top level. In terms of realism, Motion- R1 yields competitive FID of 0.201, comparable to recent strong baselines (e.g., MotionGPPT22
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0.191, MotionGPT: 0.232). Motion-RI also attains the lowest MM-Dist (2.854), indicating perior alignment the joint motion-languae embedding space, and the highest Diversity score (10.026), suggesting better modeling of the one-to-many nature of text-to-motion. Its MModality score (2.317) is likewise competitive with top methods. Together, these results indicate that the com- bination of decomposed CoT supervision and RI Binding yields motions that both semantically aligned and high-fidelity.
On KIT-ML, Motion-RI consistently leads the comparators retrieval and fidelity metries: R- Precision @ 1/2/3 = 0.431 / 0.638 / 0.761 (all best), and FID 0.287 (best). While MM-Dist (3.196) not the lowest the table, Motion-R maintains strong Diversity (10.875) and solid MModal- ity (2.262), demonstrating robust generalization across a dataset with different statistics. Overall, Motion-RI provides favorable trade-off between semantic alignment, motion realism, and diver- sity on both benchmarks, empirically supporting the effectiveness of the Decomposed CoT Data Engine and RL. Binding.
4.3 QUALITATIVE ANALYSIS AND VISUALIZATION
To complement the quantitative evaluation, we provide qualitative comparisons between Motion- RI and existing state-of-the-art methods. We divide this section into two parts: (1) in-distribution prompts from standard benchmarks, and (2) out-of-distribution instructions that require composi- tional reasoning and generalization. These visualizations highlight the advantages of Motion-RI producing semantically coherent, diverse, and controllable motion sequences. Note that more visualizations are provided in supplementary video.
Complex caption-OOD
Abstract caption-O00
A person, jumped up happily, raised his hand and excitedly.
Walking slowly along the path shaped like on infinity symbol
Figure 4: Motion-R1 results cittof-ddittrbutionn prompts. Left: Complex caption with multi- step reasoning. Right: Abstract caption requiring semantic understanding.
In-Distribution Visualization. We visualize results and compare Motion-RI with MotionLLM as shown in Figure 3 (left). Motion-R1 generates smooth, coherent sequences that respect spatial and temporal semantics for example, producing continuous circular walk with natural timing, while MotionLLM Wu et al. (2024) often fails to complete the circle or exhibits abrupt stops.
Diittr-DDisiiittionn Visualization. To evaluate generalization, we show two out-of-distriburte prompts Figure E (middle, right). For "After hearing loud noise.."" Motion-RI clearly sepa- rates reaction, retreat, and re-approach; MotionLLM merges steps omits gestures. For "A person serving in badminton," Motion-RI produces a plausible serve motion with arm lift and forward strike, while MotionLLM generates generic repetitive movements. More examples in Figure further demonstrate Motion-RL Rl's ability to interpret abstract instructions and maintain temporal co- herence. All results generated without processn.....ng.
These examples show that Motion-R generalizes well to unseen instructions through explicit CoT- style reasoning, whereas baseline methods struggle with long-horizon dependencies and abstract

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actions. To further validate these observations from human perspective, conducted user study evaluating semantic consistency and motion fluency. As detailed in Appendix [A.5] our method was significantly preferred by users state-of-the- basclines, confirming its superior perceptual quality.
4.4 ABLATION STUDY
To assess the contribution of each component in our framework. we conduct an ablation study on the HumanML3D dataset, as shown in Table 2 Specifically, we evaluate the impact of three key components: Decomposed CoT Data Engine and RI. Binding (the semantic similarity reward (Reem), and the motion similarity reward (Rmotion)).
All ablated variants achieve comparable performance, showing the robustness of our design. Using the Decomposed CoT Data Engine alone yields the weakest results (R-Precision Top-1: 0.340:t.002, FID: ..15), while adding either Rem or Rmotion significantly improves alignment (Top-1: 1: 0.483= FID: 0.281+.008).
With all components combined, the model reaches the best or second-best performance across most metrics, including the lowest FID (0.201=.004) and highest Diversity (10.026+t.075) These sults confirm the complementary roles structured reasoning and semantic/mooioon-eeel rewards in producing diverse, semantically faithful, and high-quality motion.
Table 2: Ablation study HumanML3D Guo al. (2022a). CoT, Rsem- and mottion denote De- composed CoT Data Engine, the semantic similarity reward, and the motion similarity reward, re- spectively, with additional columns showing effects of self-verification mechanism and LLM choice for Decomposed CoT Data Engine. Best results are highlighted in bold and the second best in
underline.
CoT
Ruen
Rmisson
Verification
CoT L1M
R Precision
FID
MM-Dist
Diverityy
MModality?
Top 2
Top
Deepeeek-RI
0.340=.032
0.50g
0.80ge
0.530=
9
4.762--44
Deepsekk-B RI
0.1825.03
1.60020000
0.799=.02:
0.207
2.963
9.5375-021
2.51 2.517=-015
Deepseek R1
0.38g 4.38344438
0.696
0,799
0.28194406
8.94
9.848 002
1.90g
Deepseek-RI
0.48g=-422 A02
B.688-.00
2002
0.234*-400
3.127
9-244
2.308 076
GPT 4o
0.520 0.520
0.70g
0.812
0.211
2.806
9. 1.991
2.445
0.322 436
0.497
0.610
1.706-4844
4. 4,206 9-100
0.365 0.385
0.526 on
0.627
0.700
4.130
9.711
4.067#-230
0.305
0.530
0.644
1. L.050
4.022
4.024
9.720
4.008
DeepseckRR 0.515=
0,719
009
0,201 400
2,854 0.0
10,026
0775
2.317
We also examine the effects of self- verification mechanism and different LLM choices for Decom- posed CoT Data Engine. For self celf-verification mechanism, Manual evaluation of approximately 500 randomly sampled data points from the HumanML3D dataset reveals that the initial error rate in decomposed CoT generation around 20%, primarily due to format violations, hallucinations, re- dundant steps and repetitions. After self-verification, the error rate decreases approximately 3%. The ablation results show that without self-verification, performance drops significantly, while self- verification restores optimal performance. Regarding LLM choices, experiment using GPT-4o Hurst et al. 2024) shows competitive results compared to Deepseek-R [Guo et al. (2025), confirming that our framework's effectiveness stems from the overall design rather than dependency on particular model selections.
5 CONCLUSION
In this work, we introduced Motion-R1, a unified framework for text-to-motion generation that integrates a Decomposed CoT Data Engine and RL Binding. The Decomposed CoT Data En- gine generates structured, step-by dtep-by-step reasoning traces to decompose complex language into inter- pretable action plans, while RL Binding optimizes motion synthesis through GRPO with semantic and motion-level reward alignment. Together, these innovations enable coherent, semantically faith- ful, and high-quality motion generation without relying on costly human annotations, improving controllability, diversity, and generalization.
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6 ETHICS STATEMENT
This work introduces Motion-RI, a framework for text-to-motion generation. While the method enables high-fidelity and semantically aligned motion synthesis, shares general risks associated with generative models, including the potential misuse for creating misleading, unsafe, or inappro- priate motion sequences. We strongly discourage such applications and emphasize that Motion-F R1 is intended strictly for research, educational, and other socially beneficial puposes. Users should exercise caution, ensure compliance with ethical guidelines, and consider privacy and consent when applying the model to real-world scenarios.
7 REPRODUCIBILITY STATEMENT
We committed ensuring the reproducibility of our work. Details of the framework, training protocols, and hyperparameters are provided in Section Subsection [4.] and Appendix [A.2] A.3 Code for method will be made publicly available support replication and future research.
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A
APPENDIX
A.1 STATEMENT ON THE USE OF LLM
Large Language Models (ChatGPT by OpenAl) were used exclusively to improve the clarity and fluency of English writing. They were not involved research ideation, experimental design, data analysis, or interpretation. The authors take full responsibility for all content.
A.2 DETAILS OF PROMPT
To enable reasoning-aware motion generation, we design three distinct types prompts that cor- respond to different training phases and supervision signals. These prompts guide the LLM during both supervised and reinforcement learning stages in a progressively structured manner.
A.2.1 DECOMPOSED COT DATA ENGINE
The following prompt used in the Decomposed CoT Data Engine, which encourages the language model to generate structured reasoning traces and explicit motion planning steps based on free-form motion descriptions.
You an assistant who he helps users understand descripti ons of human motions. The user begins by describing the motion they envision, and you help break that description down into few simple descriptions of the action and show the thought
process.
## Instruction:
1. User-Provided Description: The user's description may include both straightforward and abstract descriptions of human motion, such as "A person ties their shoelaces" or "A person waves goodbye."
2. Reasoning process: For each human motion description, you first think about the reasoning process in the mind to decide how to break down the description into simple, atomic motions. Ensure that each atomic motion is independent and focuses on straightforward, non-elaborate motion description. If user's s description is already simple and atomic, you don't need to break it down. Show your work in <think> </think> tags. Think step by step inside <think> tags. Then generate motions matching the atomic motion descriptions.
## Response Format:
Your response should include step-by-step thinking process: <think>, Boonnkk...<</tnnii </think>>>
## Examples:
### Example 1:
User: "a person waves goodbye."
Assistant:
<think> The user wants me to break down the action "a person waves goodbye" into simpler steps if necessary. Waving goodbye is already a fairly atomic action, as describes single, recognizable motion. Therefore, the correct response would be:
1. wave hand. </think>
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#44 Example 2:
User: "a person ties their shoelaces and stands up."
Assistant:
<think> The user wants me to break down the description "a person ties their shoelaces and stands up" into simple steps. First, need to visualize tying shoelaces: it involves bending down, grabbing the laces, tying them, and then standing up. Since "tie shoelaces" can be kept as a single action for simplicity, I'll treat that one step. Then comes standing up.
Let me ensure temporal and logical order: first bend down and tie, then stand up. Each step is independent and physically distinct.
Therefore, the correct response would be:
1. bend down and tie shoelaces.
2. stand up.
</think>
## Example 3:
User: "a person opens a door and enters the room."
Assistant:
<think> The user wants me to simplify the motion "a person opens a door and enters the room." " This involves first manipulating the door, then walking through the doorway. I should break this into: (1) reach and grasp the doorknob, (2) pull or push the door, (3) walk forward through the doorway. Check that each
simple motor action. Yes, these three steps should be clear and sequential. Therefore, the correct response would be:
1. reach and grasp the doorknob.
2. open the door.
3. walk forward through the doorway. </think>
A.2.2 MOTION-R1
Used in the final training stage, this unified format integrates both reasoning and motion generation to reinforce semantic decomposition and executable token prediction jointly.
You are an assistant who helps users generate human motion representations. The users will describe motion, your job is to break it down into a short sequence of atomic physical actions. Show your reasoning inside <think> </think> and output motion in <Motion> </Motion> tags.
Response Format: Let me think step by step. <think>.. biink....///hnkk. <Motion>.. .etiiin........ <//ootion>>
A.2.3 MOTION-RI w/o DECOMPOSED CoT
This form is used for training w/o decomposed COT. No reasoning steps are involved, and the model directly maps textual descriptions to motion token sequences. This prompt is structurally simple and helps the model learn basic text-to-motion alignment without reasoning supervision.
You are an assistant who helps users generate 3D human motion representations. The users begin by describing the motion they envision. Show output motion in <Motion> </Motion> tags. Response Format: seriiion....M.iii.....
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Published as a conference paper at ICLR 2026
A.3 EXPERIMENTAL SETTINGS
A.3.1 EVALUATION METRICS
Following Guo al. (2022a); Zhang al. (2023a): Wu et al. 2024), our evaluation metrics
summarized as follows:
1) R-Precision. It compares each motion sequence with 32 textual descriptions of one true match and randomly sampled negative examples. Retrieval accuracy is mcasured by checking whether the correct description appears within the top-1, top-2, top-3 nearest neighbors in the ranked list.
2) FID. FID measures the distributional similarity between real and generated motion features. Let E,, and (gg) be the means and covariances of real and generated feature distributions, respectively. The FID score is computed as:
FID = ||, Ma 11 + Tr(2, Mi 2(2irig))
(6)
3) Diversity. To assess the variation within generated motion sequences, we follow the protocol introduced by Guo al. Guo et al. (2022a). Specifically, we randomly sample Sas pairs of motions from the generated set, and compute the average & distance between each pair's motion features. Let fpedd and S fpred, denote the feature embeddings of the i-th sampled pair, the diversity is defined as:
Sa
Diversity =
Sppedd - Spred, ll2
Sais
In our implementation, we set Sais = 300 to ensure stable and comparable estimates across methods.
4) Multimodal Distance (MM-Dist). MM-Dist evaluates the alignment between generated motions and their corresponding text descriptions. It is defined as the cosine similarity between the embed- dings of generated motion and its conditioning text, extracted using a pretrained joint encoder.
5) Multimodality (MModality). Multimodality assesses the model's ability to generate diverse mo- tions conditioned on the same textual input. It is computed by generating multiple motions from a single description and measuring the average pairwise Euclidean distance between their feature embeddings. Higher scores indicate greater ability model one-to-many mappings between text and motion.
A.3.2 TRAINING DETAILS
We first fine-tune the pre-trained model on MotionCoTT data using supervised learning with batch size of and learning rate of 1 x 10-1, scheduled by cosine decay.
Building on this, GRPO training is implemented with group size G = 8, clipping range = 0.2, and KL penalty coefficient 0.001 for stable policy optimization.
A.3.3 EXPERIMENTS COMPUTE RESOURCES
experiments were conducted on a server with SxNVIDIA H20 GPUs. The SFT training took approximately 2 hours 8 GPUs, while GRPO-based reinforcement learning required around hours under the same configuration. Evaluation on HumanML3D and KIT-ML datasets consumed about 1 GPU-hours per run, repeated across 20 trials for statistical robustness.
Inference time analysis was conducted under the same NVIDIA H20 and prompt conditions. Over 10 runs, method takes 2.23 seconds while MotionLLM Wu et al. (2024) takes 2.11 seconds, showing no significant difference in inference time. Despite comparable inference speed, our method achieves superior performance in both quantitative and qualitative evaluations.
A.4 QUALITATIVE RESULTS ON BABEL
Results BABEL. Table reports results the BABEL Punnakkal et al. (2021) dataset, which contains long, multi-label activity annotations. Motion-RI outperforms prior methods across all metrics. It achieves the highest R-Precision 0.536++0004) surpassing InfiniDreamer (0.522+-.088)
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Published as a conference paper at ICLR 2026
and other baselines, indicating stronger text-motion semantic alignment. In terms of motion fi- delity, Motion-RI attains substantially lower FID of 0.53 .006, nearly halving the best baseline (1.14#.05 from Double TaubbeTTkc/mfiiiDeeemeet RI also sets the best score (6. 16+.1.16 141), suggesting better cross-modal consistency. Moreover, its Diversity (8.90++.095) exceeds ground-truth motion (8.52), reflecting the ability to synthesize varied yet realistic motions.
For motion-lande language embedding distance, Motion-
Overall, these results demonstrate that Motion-RI generalizes effectively BABEL, producing semantically accurate, diverse, and high-fidelity motions under complex multi-label activity settings.
Table 3: Quantitative results of Motion-RI on BABEL Punnakkal et al. (2021). The evaluations are conducted 20 times to obtain 95% confidence interval. Best results are highlighted in bold and the second best in underline.
Methods
R-Precision f
FID
MM-Dist 4
Diversity
Ground Truth
0.629 000
0,0004#000
3.51#.01
8,52
TEACH Athanasiou et al.
(2022
0.461 0.461#.012 ..02
1.43
7.93
7.71
Double'Tate Shafir et al.
20233
0.483##.00 .009
1.14
6.97#.01
8.28=t.09
DiffCollage Zhang et al.
2023b
0.487
1.83#.05
6.74#.01
7.89
InfiniDreamer Zhuo et
(2024
0.522
1.14
6.35
7.97+.05
Motion-R1
0.536 4004
0.53
1.14
8.90
A.5
USER STUDY
To evaluate the perceptual quality of our generated motions, conduct user study comparing our method with baseline approaches. We randomly select 100 composite action descriptions as test samples, where each description's generated results from three methods (MotionAgent Wu et al. (2024), MoMask Guo et (2044), ours) are displayed side-by-side. Fifty users independently select the best method for semantic consistency and motion fluency respectively for each description. The results are shown in Table 4 demonstrating that our method significantly outperforms baselines in both semantic consistency and motion fluency.
Table User study on semantic consistency and motion fluency. 100 composite action descrip- tions were evaluated by 50 users, who independently selected the best method for each dimension. Best results are highlighted bold and the second best in underline.
Methods
Semantic Consistency
Motion Fluency
MotionAgent Wu et (2024)
28.4%
32.6%
MoMask Guo et al. (2024)
25.8%
28.2%
Motion-R1
45.8 %
39.2%
A.6 LIMITATIONS
Despite its advantages, Motion-RI has limitations. The Decomposed CoT Data Engine relies on general-purpose LLMs, which may produce noisy or suboptimal plans under ambiguous instruc- tions. Furthermore, while RL Binding streamlines policy optimization, careful design of motion- and semantic-level rewards remains crucial. Future work will explore adaptive reward learning and interactive feedback mechanisms to further enhance motion quality and robustness.
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Reviews:
[{"review_id": "oJmUlV1Abr", "reviewer": "Reviewer_K5ke", "summary": "This paper introduces Motion-R1, a novel framework for text-to-human-motion generation. It addresses key limitations in temporal reasoning and motion quality of existing methods through two core innovations: firstly, a Decomposed Chain-of-Thought Data Engine that leverages large language models to automatically break down complex language instructions into structured, step-by-step action plans, enhancing the model's understanding of temporal and causal relationships; secondly, an RL Binding strategy that integrates multi-modal alignment directly into the reinforcement learning reward function, using motion similarity, semantic similarity, and format rewards to guide the model towards generating motions that are both realistic and semantically faithful to the text, without relying on costly human annotations. Extensive experiments on multiple benchmark datasets including HumanML3D, KIT-ML, and BABEL demonstrate that Motion-R1 achieves state-of-the-art performance across key metrics such as R-Precision, FID, and Diversity, validating its superior capability in generating high-quality, semantically-aligned, and diverse motion sequences.", "questions": "1. Despite using DeepSeeker R1 for filtering, the CoT data generated by LLM inevitably contains errors or subjective biases. Has there been a manual sampling evaluation of the automatically generated CoT data? What is the approximate error rate? What observable effects do these noises have on model training?\n2. The paper proposes three rewards: format, motion, and semantics. How are the weights of these rewards determined? Is it through grid search, empirical setting, or adaptive methods? Is there any experiment showing the sensitivity of model performance to these weights?\n3. The paper demonstrates excellent performance on OOD instructions. Are there any specific types of instructions (such as highly abstract, requiring physical knowledge, or multi-role interaction) that Motion-R1 still struggles to handle? Can its failure modes be systematically defined?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Skillfully combines the Chain-of-Thought paradigm from natural language processing with reinforcement learning, providing a novel and effective framework for applying large language models to complex motion generation tasks.\n2. Not only achieves performance improvements but also provides transparency into the generation process through structured CoT reasoning steps, enhancing model interpretability and controllability—crucial aspects for practical deployment.\n3. Directly tackles two core challenges in text-to-motion generation, temporal causal reasoning and motion quality optimization, by proposing practical solutions, demonstrating the authors' deep understanding of the field's fundamental problems.", "weaknesses": "1. The overall framework performance is highly dependent on the quality of CoT data generated by LLMs. Errors in LLM's understanding of certain actions could be amplified in subsequent processes, yet the paper lacks systematic analysis of such error propagation.\n2. The three reward functions (format, motion, semantic), while intuitive and effective, may not cover all important aspects of motion generation, such as physical plausibility, energy consumption, style consistency, and other nuanced quality dimensions.", "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": "8CpDiYc8H1", "reviewer": "Reviewer_8NnX", "summary": "Motion-R1 is a lightweight RL framework for text-to-motion generation that circumvents costly human-annotated rewards. An LLM-powered decomposed chain-of-thought engine first converts high-level captions into temporally ordered, fine-grained reasoning sequences, yielding abundant synthetic supervision. Reinforcement learning is then reduced to a simple binding stage that optimizes a composite reward combining motion-similarity against ground-truth and semantic-similarity to the text. Extensive experiments on HumanML3D, KIT-ML and BABEL show that Motion-R1 attains state-of-the-art R-Precision, FID and MM-Dist while requiring no human preference data.", "questions": "1. How does the framework address the high variability in CoT decompositions for complex prompts like \"doing tai-chi\"?\n2. Does the author have specific mechanisms to enforce consistency or select a single \"best\" decomposition from many variants? What are the criteria for this selection?\n3. Have the authors considered releasing the generated CoT dataset or the filtering scripts to allow for further analysis of this issue by the community?\n4. Have the authors analyzed the failure cases that arise specifically from this data inconsistency? For instance, when trained on varied decompositions for the same concept, does the model generate hesitant, \"averaged,\" or nonsensical motions? Showing such examples would provide valuable insight into the model's limitations.\n5. Have the authors quantified the impact of external LLM quality on the final motion generation quality? For example, was a study conducted comparing the model's performance when trained on the \"raw\" noisy CoT dataset versus a manually cleaned or consistently-decomposed subset? This could help measure the model's sensitivity to the quality of the generated data.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Elegant and Effective Methodology: The core contribution—combining an automated CoT data generation pipeline with a streamlined RL mechanism—directly addresses a clear limitation of prior end-to-end models, which often struggle to interpret and execute multi-step or complex instructions. \n2. Eliminates the Need for Human Annotation: RL Binding obviates the need for costly, time-consuming, and often subjective human preference labeling. By cleverly using the existing ground-truth data (text and motion) to formulate the reward function, the framework becomes more scalable and efficient.\n3. Strong Empirical Performance: The paper provides robust empirical evidence to support its claims. Motion-R1 achieves state-of-the-art results on multiple standard benchmarks. The quantitative tables show consistent improvements, and the qualitative visualizations (Figure 3) are particularly compelling, clearly illustrating the model's superior ability to handle complex, out-of-distribution prompts compared to a strong baseline (MotionLLM).", "weaknesses": "Dependency on External LLM Quality: The performance of the entire framework is fundamentally tied to the reasoning and decomposition capabilities of the LLM used in the Decomposed CoT Data Engine. The paper acknowledges that the LLM can produce \"noisy or suboptimal plans,\" which could introduce errors into the training data. This dependency might limit the reproducibility and robustness of the approach if a different or less capable LLM is used.\nInconsistency LLM output across multiple decompositions: A prompt can be decomposed into different numbers of steps with varying content and granularity across multiple generation attempts. This phenomenon can be readily reproduced by reviewers and readers. Such inconsistency introduces noise and ambiguity into the training data, as the model is taught that a single instruction can map to many different, and sometimes conflicting, action plans. This could lead to the model learning an \"averaged\" and incoherent motion policy or producing unpredictable results at inference time.", "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": "1Pz3jbvSIw", "reviewer": "Reviewer_wKSo", "summary": "This paper presents Motion-R1, a text-to-motion generation framework that integrates a Decomposed Chain-of-Thought (CoT) Data Engine, which uses LLM-generated reasoning traces to break complex instructions into stepwise sub-actions, and an RL Binding strategy, which applies multimodal alignment rewards for motion, semantic, and format consistency to guide reinforcement learning without human preference labels. Experiments on HumanML3D, KIT-ML, and BABEL show that Motion-R1 achieves strong performance, with a 3.5% improvement in MM-Dist on HumanML3D and gains in R-Precision and FID on KIT-ML and BABEL.", "questions": "Please address my comments regarding the weaknesses of the paper.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Paper writing: the paper writing, figures and overall structure make the paper easy to follow.\n\n2. Technical novelty: The RL Binding replaces human preference modeling with automatic motion/text similarity rewards is somewhat novel, but its effectiveness are not probably evaluated (see weaknesses).\n\n3. Good quantitative results: Based on the reported results in Table 1, Motion-R1 achieves consistent gains across major metrics (yet, the improvement is not significant and worse in some metrics).", "weaknesses": "1. Unfair and incomplete comparison with Motion-Agent: The paper compares Motion-R1 only against MotionLLM, which is merely one internal component of Motion-Agent (Wu et al., 2024). Motion-Agent integrates MotionLLM with GPT-4o for reasoning, task decomposition, long-sequence composition, and interactive motion editing. Thus, the comparison omits the very agent capabilities that Motion-R1 aims to emulate with its CoT Data Engine and RL Binding. Claims of superiority are therefore not substantiated. Notably, this is not the first work that propose motion decomposition in the text-to-motion generation literature.\n\n2. No evaluation against GPT-4o or similar reasoning LLMs: Both the CoT Data Engine and RL Binding could be replaced by prompting a powerful multimodal model like GPT-4o, which can already perform temporal decomposition and semantic alignment in a zero-shot way.\nIt is unclear whether Motion-R1’s improvements derive from genuine algorithmic novelty or simply from additional fine-tuning.\n\n3. Lack of evidence for RL Binding effectiveness: While RL Binding is presented as the key to text–motion alignment, the ablation in Table 2 is limited, as it simply removes this component from the framework. A fairer comparison would replace it with a non-RL or purely supervised variant (using existing text–motion pair datasets for training, as done in MotionGPT, MoMask, or MotionLLM). Without such comparisons, it is impossible to determine whether RL Binding materially improves alignment or merely introduces minor regularization.\n\n4. No mechanism or evaluation for temporal smoothness between sub-motions: Because Motion-R1 decomposes a description into multiple sub-actions, a crucial question is how continuity and smooth transitions between these segments are enforced. The paper does not describe any temporal-smoothing module, blending loss, or transition constraint, nor does it evaluate transition quality. This raises practical concerns: even if each sub-motion is semantically correct, discontinuities between them could produce unrealistic or jerky results.\n\n5. Scope and novelty: The proposed framework mainly recombines existing elements: LLM-based reasoning, VQ-VAE motion tokens, and RL optimization, without introducing a fundamentally new generation paradigm. Its novelty claim relies on integration rather than on new algorithmic insight, and the unfair comparisons and evaluations weakens its empirical significance.", "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": "KZJsvFRGve", "reviewer": "Reviewer_YjHi", "summary": "The paper introduces a novel framework called Motion-R1 for text-to-motion generation. It combines Decomposed CoT Data Engine and RL Binding. First it uses LLM to produce step-by-step motion descriptions. This helps enable better causal modeling. Then RL binding achieves multi-modal alignment rewards(motion similarity, semantic similarity, and format rewards) to improve the motion realism and semantic fidelity without human annotations. The method uses HumanML3D, KIT-ML, and BABEL datasets for evaluation, achieving competitive results across multiple metrics.", "questions": "- What is the time consumption for generating a single motion sequence with your method?\n\n- Would using different LLMs have an impact on the results of your method?\n\n- How does the innovation of your method compare to the five RL+Motion generation methods (**AToM**, **InstructMotion**, **ReinDiffuse**, **MotionRL**, and **RLPF-MA**) I mentioned?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- Motion generation tasks have been struggled with generating long-sequence motions effectively. This paper innovatively addresses this issue by integrating CoT with motion generation.\n- The RL Binding mechanism considers multiple reward signals. It prevents the generated motions from over-optimizing toward one specific aspect.\n- The overall framework is highly innovative and interpretable, with a very natural idea. \n- The experimental results are highly convincing, demonstrating competitive performance across three mainstream datasets.", "weaknesses": "- The experiments are insufficient in terms of quality evaluation, as it provides few visual cases for **comparison** of different methods.\n\n- The experimental section does not report the time consumption for generating a single motion.\n\n- The article lacks a comprehensive review of methods combining human motion generation with reinforcement learning. Some approaches that employ an RL + motion generation framework, such as **AToM**, **InstructMotion**, **ReinDiffuse**, **MotionRL**, and **RLPF-MA**, were not mentioned in the article. I am aware that some of these methods are currently on Arxiv and have not yet undergone peer review. I am just offering a friendly reminder for the authors to **clearly outline the development trajectory of RL + Motion generation in the related work section**, so as to better highlight the innovative contributions of your 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": {}}]

Author rebuttal:

Decision: Accept (Poster)