Pose-RFT: Aligning MLLMs for 3D Pose Generation via Hybrid Action Reinforcement Fine-Tuning
Authors: Bao Li
Xiaomei Zhang
Miao Xu
Zhaoxin Fan
Xiangyu Zhu
Zhen Lei
Venue: ICLR 2026 Poster (2026)
Keywords: Human Pose Estimation
Multimodal Large Language Model
Reinforcement Fine-Tuning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Generating 3D human poses from multimodal inputs such as text or images requires models to capture both rich semantic and spatial correspondences. While pose-specific multimodal large language models (MLLMs) have shown promise, their supervised fine-tuning (SFT) paradigm struggles to resolve the task's inherent ambiguity. Its reliance on objectives like SMPL parameter regression creates a critical alignment gap, compromising the model's ability to achieve the required semantic and spatial fidelity. To close the gap, we propose Pose-RFT, a framework that shifts the learning paradigm from supervised imitation to reward-driven reinforcement fine-tuning (RFT). We address the core technical challenge of this task: a 
hybrid action space requiring joint optimization of discrete language and continuous pose outputs. To this end, we introduce HyGRPO, a hybrid reinforcement learning algorithm that enables stable optimization by performing group-wise reward normalization over sampled responses. Pose-RFT incorporates task-specific reward functions to guide optimization towards spatial alignment in image-to-pose generation and semantic consistency in text-to-pose generation.
Extensive experiments on multiple pose generation benchmarks demonstrate that Pose-RFT significantly improves performance over existing pose-specific MLLMs, validating the effectiveness of our approach in closing the alignment gap for 3D pose generation.

Published as a conference paper at ICLR 2026
POSE-RFT: ALIGNING MLLMS FOR 3D POSE GENER- ATION VIA HYBRID ACTION REINFORCEMENT FINE-
TUNING
Bao Li Xiaomei Zhang MMia Xut.3, Zhaoxin Fan', Xiangyu Zhu Zhen Lef
MAIS, Institute of Automation, Chinese Academy of Sciences
Schooo of Artificial Intelligence, University of Chinese Academy of Sciences
"CAIR, HKISI, Chinese Academy of Sciences
'Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing.
School of Artificial Intelligence, Beihang University
(1ibao2023, zhangxiacmei2011 {miao. .u) (eairci aaaar-cas...... .rg. hk, rchaoxinf]@bual edu.cy
xiangyu. zhu, (een.lei]@ae on
ABSTRACT
Generating 3D human poses from multimodal inputs such as text images re- quires models to capture both rich semantic and spatial correspondences. While pose-specific multimodal large language models (MLLMs) have shown promise, their supervised fine-tuning (SFT) paradigm struggles to resolve the task's inher- ent ambiguity. Its reliance objectives like SMPI. parameter regression creates a critical alignment gap, compromising the model's 's ability to achieve the required semantic and spatial fidelity. To close the gap, we propose Pose-RFT, framework that shifts the learning paradigm from supervised imitation to reward-driven rein- forcement fine-tumie tne-ttunng (RFT). We address the core technical challenge of this task: a hybrid action space requiring joint optimization of discrete language and con- tinuous pose outputs. To this end, we introduce HyGRPO, a hybrid reinforcement learning algorithm that enables stable optimization by performing group- woipp re- ward normalization over sampled responses. Pose-RFi incorporates task-specific reward functions to guide optimization towards spatial alignment image-to- toreeetto-psseee pose generation and semantic consistency in text-to-pose generation. Extensive experi- ments multiple pose generation benchmarks demonstrate that Pose-RFT signif- icantly improves performance over existing pose-specific MILMs, validating the effectiveness of our approach in closing the alignment gap for 3D pose generation.
INTRODUCTION
Recent advances in 3D human pose generation (Delmas et al. 2022; 2023 2024 Miao et al. [024]]
Wang et al. 2025b; Tevet al. 2022 et al. 2025a) have increasingly focused on addressing the problem of understanding and reasoning about 3D human poses from multimodal inputs, such as images and text. Among these, ppse-specific multimodal large language models (MLLMs) (Feng ct al.) 2024 Lin et al. 2024 Li et al. (025cc have emerged as promising direction, extending general-purpose language models with dedicated pose decoders to enable joint reasoning over lan- guage, vision, and 3D pose. While these models have shown strong performance, their standard training via supervised fine-tuning (SFT) exposes a fundamental limitation.
The central challenge is the inherent one-to ane-to-many nature of 3D pose generation. This ambiguity is explicit in the text-to-pose task (Delmas et al. 2022 Li et al. 20244), where a single prompt can map the image-to-pose task (Von Marcard al. 2018 Shen et al. 2023 Yan et al. 2024] Wang et al. 2025a). a classic ill-posed problem where multiple plausible 3D poses can yield the same 2D evidence. The SFT paradigm, which learns deterministic mapping via regression to a single ground truth for each sample, is fundamentally
to broad distribution of valid poses. It is implicit
Equaa contribution. Corresponning author.

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Published as a conference paper at ICLR 2026
MLLMs for 3D Generation
Alignment Gap in Pose Generation
Distributton (One-ta
RFT
Text-to-Port
Masraged dlgh-teaard Dutpot)
(eet you Aeuele inh Aig Aun eighhhhh
that lighing eciiabe hedwords hod's
the
Image-to-Pase
Pose-RFT
de dise
Figure 1: Examples and Motivation. Left: An overview of our Pose-RFT framework for multi- modal 3D pose generation. Right: Illustrating the alignment gap. While SFT yields suboptimal averaged output, RFT produces high-reward output for superior semantic and spatial alignment.
misaligned with this property. Consequently, SFT models are driven to predict an averaged, often suboptimal, output to minimize expected error across the dataset. As illustrated in Figure I this creates a critical alignment gap between the model's predictions and the true objectives of semantic consistency and spatial accuracy.
To close this alignment gap, a paradigm shift from supervised imitation goal-driven optimization is necessary. Reinforcement Learning (RL) (Schulman et al. 2017 Rafailov et al.) (2023) offers a principled framework for this, enabling models learn from reward signals that directly reflect task- specific goals. However, applying RL to this domain presents a significant challenge: most existing
Reinforcement Fine- Tuning (RFT) algorithms (Achiam et al., 2023 Jaech et al. 2024 Liu et al.,
20256 [Shen et al.) [025 Li et al. (0225) are designed for the discrete token spaces of language and are not equipped to handle the fine-grained, continuous parameters of 3D human poses.
To address these challenges, we propose Pose-RFT, a novel reinforcement fine-tuning framework specifically designed for 3D human pose generation MILMs (see Figure 2). First, we formulate the task hybrid action space reinforcement learning problem, where the policy must simultane- ously produce discrete actions (e.g., text tokens) and continuous actions (e.g., 3D pose parameters). To effectively manage the inherent uncertainty and enable the stochastic exploration required by RL, the continuous action is modeled by a multivariate Gaussian policy. This policy is parameterized by dedicated pose head that predicts both the mean and covariance for a given state. Second, we intro- duce HyGRPO, novel online hybrid reinforcement learning algorithm designed to achieve stable optimization in the challenging hybrid action space. By leveraging group-wise reward normaliza- tion over multiple sampled outputs, HyGRPO directly optimizes the policy, effectively steering it towards high-reward responses. Third, we propose four task-specific reward functions to guide policy optimization: (i) a spatial location reward for maage-to-poss generation, (ii) semantic align- ment reward for text-to-pose generation, (iii) format correctness reward, and (iv) a text embedding similarity reward. By training with diverse outputs and structured feedback, HyGRPO encourages the model to generate 3D poses that are spatially accurate and semantically aligned.
In summary, our main contributions are follows:
(1) We propose ROSe-RFT, the first reinforcement fine-tuning framework specifically designed for 3D human pose generation in MLLMs. (2) We develop HyGRPO, hybrid-action reinforcement learning algorithm that effectively optimizes both discrete and continuous outputs in pose-specific MLLMs. (3) Extensive experiments on multiple pose generation benchmarks demonstrate that Pose- RFT significantly improves performance over existing pose-specife MLLMs, validating the effec- tiveness of hybrid action reinforcement fine-tuning for 3D pose generation.
RELATED WORK
2.1 HUMAN POSE GENERATION
Human pose generation involves producing 3D human poses conditioned on either images or text. For image-to-post generation, also known pose estimation, existing approaches are typ- ically divided into optimization- and regression-bastt methods. Optimization-base meth- ods (Bogo al. 2016. Pavlakos et al. 2019) estimate 3D pose parameters by aligning projected
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joints with detected 2D keypoints through iterative refinement. In contrast, regression-baat ap- proaches (Kanazawa et al. 2018 Cai et al., 2023 Dwivedi et al., 2024; Goel et al. 2023) rely on deep neural networks to directly predict 3D poses from input images. Text-to-pose generation aims to synthesize 3D human poses based on textual descriptions, such as physical attributes or actions (Delmas al. 2022 Tevet al., 2022 Hong al. (022). Although these methods have shown promising results, they remain confined to either image-to-pose or text-to-pose tort generation, without unified framework capable of leveraging cross-modal knowledge to infer human poses from both visual and textual inputs.
2.2 MULTIMODAL LARGE LANGUAGE MODELS
Multimodal Large Language Models (MLLMs) (Achiam et al. 2023 Liu ct al. 2023 Li et al,
2024a Wang al. 2024b Chen et al. 2024b have shown strong performance in vision-language understanding tasks by jointly modeling visual inputs and natural language. These models excel at multimodal reasoning, visual grounding, and instruction following, enabling them comprehend complex visual content in diverse application scenarios. Leveraging these capabilities, recent works have successfully applied MLLMs downstream vision-centric tasks such as image segmentation (Lai et al., 2024:] Bai et al.) 2024), anomaly detection (Gu et al. 2024), and keypoint localization WWnn et al. 2024a). demonstrating their transferability beyond purely linguistic domains.
To adapt MLLMs to downstream tasks, post-training strategies such as supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) are commonly used. Recent efforts such as ChatPose (Feng et al. (2024) and UniPose (Li et al. (202cc) have applied SFT to extend MLLMs for 3D pose gen- eration, leveraging their lasionnlanguage reasoning capabilities. However, these methods rely solely on SFT and do not incorporate reinforcement-basse optimization. The absence of RFT limits the model's 's capacity to further refine generation quality, particularly in scenarios involving ambiguity and task-specific alignment.
2.3 REINFORCHMENT LEARNING
Reinforcement learning (RL) (Sutton et al. 1998) is a core paradigm in machine learning, where an agent learns a policy-a mapping from observations to actions-by interacting with an environment and optimizing cumulative rewards. Through trial-and-error learning, the agent improves its policy based on feedback in the form of scalar rewards. Classical algorithms such as Q-learning( Watkins
& Dayan 1992) have been successfully applied in fields such as robotics, autonomous control, and game playing. With the rise large language models (Radford et al. 2018 Touvron et al. 2023
Achiam al. 2023). Reinforcement Learning with Human Feedback (RLHF) (Bai et al. 2022) has become as a key technique for fine-tuning models using human preference data. RLHF leverages algorithms like Proximal Policy Optimization (PPO) (Schulman et al. 2017) and Direct Preference Optimization (DPO) (Rafailov et al.) 2023) to guide model behavior for improving the alignment, coherence, and helpfulness in response generation.
In the context of multimodal large language models, recent works (Zhou et al. 2025 Liu et al. 2025a Zhan al., 2025 Liu et al., 2025b Yang al., 2025 Zhang al.) 2025) Shen et 2025) have explored the use of RL with verifiable reward signals to enhance visual reasoning. However, the application of RL to 3D human pose generation remains underexplored, primarily due to the continuous nature of pose regression, which poses challenges for RL methods originally designed for discrete action spaces. To address similar challenges in other domains, several works have proposed
hybrid contrite-ooninuuuss action formulations (Lowe et al., 2017 Fan et al. 2019 Li et al. 2021 offering promising direction for adapting reinforcement learning to structured continuous tasks such 3D pose generation.
3
METHODOLOGY
This section first reformulates 3D human pose generation reinforcement learning problem under a hybrid action space. It then introduces the proposed Hybrid Action Space Group Relative Policy Optimization (HyGRPO) algorithm, which jointly optimizes discrete language and continuous pose outputs. Finally, it describes how HyGRPO is applied to fine-tune pose-specific MLLMs using task-specific reward functions designed for 3D human pose generation.
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Pose-RFT: Reinforcement Fine-Tuning for Pose-Specific MLLM
Pose-Specific MLLM as Hybrid Policy
Training
Pase-Specift Policy Model
Reward Function
Policy Optimication
Textt Encoder
Projecter
Sample
Generate Candidates
Compute Advantages
Update Policy
Input
Large language Model
LORA
Multimodil Input
Group of candidates
Advantages
HyERPO Update Step
Largsage
Spece
Whattiche oor delggin bellnge conyon gleene ASSMIT poie
Discrete Prota Continnous Probs
Sample Polrdd
g3 PPOBS
Pi
libe
A
(10.3, io
Language Head
Pose Head
0g SMP. ol ran PPBB.
A (+0.2, 0.5)
P2
A
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Contiuun
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Fox
For dlrt
Ag [-0.5, Null!
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weln, - mg(olg) my(plp
Rybrid Policy
Figure 2: Overview of Pose-RFT Framework. Our reinforcement fine-tuning framework for pose- specific ML..Ms. Given a multimodal input, the model generates multiple hybrid responses (text pose). These candidates are evaluated using task-specific rewards, and our HyGRPO algorithm updates the policy to promote the generation of higher-reward outputs.
3.1 REFORMUILATING 3D POSE GENERATION FOR REINFORCEMENT LEARNING
We formulate the 3D human pose generation within pose-specific MLLMs hybrid action inforcement learning problem. The model operates in a hybrid action space comprising discrete language tokens and continuous 3D poses. The overall policy is defined
To(a, plg) = To(alg) To a),
(1)
where g denotes multimodal input, a represents discrete textual responses, and p denotes contin- uous 3D pose parameters. We regard the joint distribution Tę plq) as the overall policy, which is factorized into discrete sub-policy To (a q) modeling the distribution over textual responses, and continuous sub-policy xo (plg. a.) modeling the distribution over 3D poses conditioned on both the input query and the generated language response.
To parameterize the continuous policy, we adopt multivariate Gaussian distribution defined over space 3D human poses:
Telplg, a) Np; (a a), Eo(g, a)),
where the mean ullq, a) and covariance [g a) are predicted by a continuous pose head condi- tioned on the multimodal input q and the discrete response This probabilistic formulation captures the aleatoric uncertainty inherent in 3D human pose generation by modeling the conditional distri- bution over continuous pose vectors. Furthermore, the use of differentiable multivariate Gaussian enables both stochastic sampling during training and efficient gradient-based optimization within the continuous pose space.
Benefiting from the strong cross-modal alignment established during MILM pretraining, both the discrete and continuous policies are built on shared language-aligned multimodal embedding space. To further enhance this representation, we augment the MLLM with pose-aware encoder, Vision Transformer pretrained on pose estimation, to extract more informative and rose-relevant vi- sual features. These features are fused with the language-ale aanuuage-aligned multimodal embeddings to yield more informative and pose-relevant state space for reinforcement learning. Details of the pose-aware encoder and the visual fusion strategy are provided in Appendix [A] Taken together, the architectural enhancement from the pose-aware encoder and the probabilistic modeling from the Gaussian policy establish powerful and robust baseline model, which we then elevate further using reinforce- ment fine-tuning framework.
3.2 HYGRPO: HYBRID ACTION SPACE GROUP RELATIVE POLICY OPTIMIZATION
To optimize the hybrid policy defined in the previous section, introduce Hybrid Action Space Group Relative Policy Optimization (HyGRPO). an online reinforcement learning algorithm de- signed to enhance pose-specific MILLMs for 3D human pose generation. HyGRPO is designed to
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operate directly on the hybrid action space, jointly optimizing the discrete language and continuous pose heads within the shared MLLM embedding space. By doing so, facilitates coherent align- ment between the model's 's textual and pose outputs, effectively bridging the gap between standard discrete token prediction and continuous parameter generation.
To handle hybrid outputs, HyGRPO models the policy me both discrete text answers and con- tinuous human poses p conditioned on input question q. For each training sample from dataset D, we sample G output candidates {a,, Pi pi Ta (=(-))). and optimize the policy using the following objective:
1 G
E (ar (l
(0)A
where r0) the importance weight of the i-th sampled output, computed as the ratio between the current policy and the reference policy:
r+) = Ta(a, Prlg)
<a(a;1g)
(pilg. a,)
(ar P:l9) (ar(ga)))
Tec(Pi a,)
ra(a:19)
re(pilg.ae)
key design choice in HyGRPO the decomposition of the normalized advantage, Å, into discrete and continuous components, which measure the quality of the textual response and the predicted pose, respectively:
Ålq, a) = F(g.a) a)
aq.
(5)
discrete advantages
continous advaniages
where F measures the quality of the generated textual response, and Å evaluates the predicted pose quality. To ensure training stability, we adopt clipped importance sampling as in PPO (Schulman al. 2017). The final HyGRPO training objective is:
JyYGRPO =By (ar. (
min((afi, Ciipra 1-e, l+t)
T
(6)
Σ min(rrA,, clip(rr, 4oir - (Tol(TTTTT))
where G' the number of generated candidates, V is the subset of candidates that include valid pose output. This objective provides separate, targeted advantage signals for the discrete and continuous policy heads, enabling stable and generalizable training across the hybrid action space. The full derivation and algorithm are detailed in Appendix and
3.3 GUIDING HYGRPO WITH
REWARDS
The HyGRPO algorithm guided by suite of task-specific reward functions designed on a core principle: each reward provides targeted feedback for distinct component of the MLLM's hybrid output. This modular design ensures comprehensive supervision, governing not only the continuous pose outputs (for spatial and semantic accuracy) but also the discrete textual outputs (for conver- sational format and correctness). This approach is crucial as it enhances the model's new pose generation capabilities while preserving its foundational conversational capabilities.
Spatial Location Reward in Image-to-Pose Generation. In the image-to mage-to-pose generation task, the model is expected output SMPL pose coefficients conditioned the input image. To encour- age spatial accuracy, the reward should reflect how well the predicted pose aligns with the visual input. Following standard 3D human pose estimation practice, this reward defined as the inverse of the mean joint position error the Euclidean distance between predicted and ground-truth 3D joint locations:
Risint
| peed J
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Semantic Alignment Reward in Text-to-Pose Generation. In the text-to- proo-posee generation task, the model is expected to predict SMPL pose coefficients conditioned on a text prompt. Unlike image-to- generation, which emphasizes joint-level accuracy, this task focuses high-level semantic alignment between the textual description and the generated pose.
To quantify this alignment, we adopt pretrained text-pose retrieval model that maps both inputs into a shared embedding space. Specifically, the retrieval model comprises a text encoder (x(+) and a pose encoder (p()). both projecting their respective inputs into shared embedding space. The semantic alignment reward is defined as the similarity score between the encoded text and the generated pose:
R Remantti = cos((e(g))))))))))))))))))(()) (p))).
(8)
Format Reward. To encourage the model to generate responses that conform to specified for- mat, we introduce format reward, denoted as Riomaa- For instance, expect the model to produce outputs enclosed in a template such as: "The SMPL pose of this person is <POSE>." To enforce this constraint, we apply regular expression matching assess format compliance. The format reward defined as:
1, the output matches the expected format
Format
0,
otherwise
Text Embedding Similarity Reward. To preserve general QA capabilities while fine-tuning for pose-centric tasks, incorporate text reward that encourages semantic agreement between gen- erated and ground-truth answers in languale QA tasks. Specifically, we utilize the BGE-M3 encoder (Chen et al. (0244)) compute dense embeddings for both the model-generated answer and the ground-truth response. The reward is defined as the cosine similarity between the normalized embeddings of the predicted and ground-truth answers:
Ree cos ((ppeaa))))) B(ag)).
(10)
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. To train Pose-RFT, we incorporate four types of data sources to enhance multimodal understanding: (1) Text-Pose Data. We utilize the PoseScript dataset (Delmas al., 2022). which provides natural language descriptions paired with 3D human poses. This enables the model to learn fine-grained semantic correlations between language and human poses. (2) Image-Pose Data. Fol- lowing prior works (Goel et al. 2023 Feng et al.] 2024 Li et al. 2025c). we adopt standard human pose estimation training datasets, including Human3.6. 3.6M (lonescu et al. 2013). MPI-INF-3DHP (Mehta et al., 2017). COCO (Lin et al. 2014), and MPII (Andriluka et al. 2014). For evaluation, we use the 3DPW (Vnn Mareard al.) 2018) and Human3. 6M test sets. (3) Image-Text Data. We employ the BEDLAM-Script dataset introduced in PoseEmbroider (Delmas al. (024)) curated multimodal dataset containing images, 3D poses, and texuual descriptions, constructed based on the
BEDLAM dataset (Black et al.) 2023). (4) VOA Data. For visual question answering, utilize
the LLa V--Instruct- 150k dataset (Luu et al. 2023
Metrics. We evaluate our model on both image-to-poss tope and text-to-pose tasks using reconstruction and retrieval metrics. Image-to-Pose Reconstruction Metrics: We report the Mean Per Joint Po- sition Error (MPJPE) and the peccusttss-alineed MPJPE (PA-MPJPE), which measure the average Euclidean distance between predicted and ground -ruth joint positions, with and without Procrustes alignment, respectively. Text-to-Pose Retrieval Metries: Following (Feng al. 2024 Lin al.)
2024 Li et al. 2025c), we report Recall @ K (K = 5, 10, 20) for both text-to-pose (RT2F)) and pose- to-text (R retrieval tasks, which assess the accuracy of matching poses with their corresponding textual descriptions.
Implementation Details.
We adopt LLaVA-1.5V-7B (Liu et al. 2023) as the vision- -aioun-language backbone. For the pose-aware encoder, we employ the pretrained Vision Transformer from HMR2.0

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Table 1:
Comparison on Human Pose Estimation task. Reconstruction errors are reported on the
3DPW and Human3. 66 datasets.
3DPW
Human3.6M
RPE
Method
MPJPE PA-MPJPE MPJPE PA-MPJPE MPJPE PA-MPJPE
HMR (Von Marcard et 2018
130.0
76.7
88.0
56.8
SPIN (Kolotouros et al. 2019
96.9
59.2
62.5
41.1
244.9
107.3
PyMAF (Zhang et al. 2021
92.8
58.9
57.7
40.5
HMR2.0 0 (Goel al.
2023
70.0
44.5
44.8
33.6
225.2
105.7
MEGA (Fiche et al.
2024
67.5
41.0
TokenHMIR (Dwivedt et al.
2024
71.0
44.3
ChatPose (Feng al. 2024
163.6
81.9
126.0
82.4
275.0
101.8
UniPose [i ct al. 2025c)
94.7
59.1
69.2
41.8
213.4
94.1
Pose-RF1 (Ours)
85.9
51.6
63.0
44.5
198.6
87.0
Table 2: Comparison on Text-to-Pose Generation Task. Retrieval metrics (Recall@K, K, K-5, 10, 20) reported the PoseScript dataset under two evaluation protocols.
PoseScript (Full Retrieval)
PoseScript (Random Sampling)
Method
R R12P t
R2T
R
1
R T2P
RPT
PoseScript (Delmas et al.
2022
40.4 52.3 65.0
41.4 54.1 65.9 73.3 82.5
89.4
70.0
82.5
87.4
ChatPose Feng et al. 2024)
17.6 25.3 35.8
28.0
39.0 54.4 39.9 50.6 58.7
56.1 65.3
72.5
ChatHuman (Lin
2024
41.8 52.6 65.1
42.1
52.3 66.5
UniPose (Li ét al. 2025c)
73.7 82.4 89.6 70.9 80.5 89.6
Pose-RFT (Ours)
42.2 53.0 65.5 45.3 57.2 70.4 71.8 82.6 88.7 74.6 86.5 91.5
Goel al. 2023). Reinforcement fine-tuning follows the settings of Visual-RFT (Liu al. (025b) and VLM-RI (Shen et al.] 2025). During both pretraining and fine-tuning, the CLIP encoder and pose-aware encoder are kept frozen, while the projector and task head are updated. The large language model is fine-tuned using LoRA (Hu et al. 2022). Further implementation details provided in the Appendix D
4.2 COMPARISONS ON HUMAN POSE GENERATION TASKS
Image-to-Pe Pose Generation. We evaluate our method on both standard reconstruction and com- plex reasoning tasks for image-to-pose generation. On standard benchmarks like 3DPW (Von Mar- card et al. 2018) and Human3.6N, (Lonescu et al. (013)) [ that RFse-RFT significantly outperforms other ML.LM-based approaches, demonstrating the efficacy of our reinforcement fine- tuning framework in closing the alignment gap. While performance gap remains compared to traditional specialist models this setting, the unique advantage of the MLLM paradigm is evident on the Reasoning Pose Estimation (RPE) task (Feng et al., 2024]. On this more complex bench- mark, which requires essual-language reasoning that specialist models cannot perform, Pose-RFT establishes new state-of-the-arr, validating the effectiveness of our MLL.M-based approach for advanced, reasoning-driven pose estimation.
Text-fo-Pose Generation. We compare Pos with existing text-conditional pose generation
models (Delmas et al., 2002 Feng al. 2024 Lin et al. 2024 Li et al.) 2025c) on PoseScript- H2 test set (Delmas et al.] 2022). Following the standard protocol for this task, we generate 3D poses from text prompts and then use a pretrained retrieval model (Delmas et al. 2022) to compute Recall @K scores
proxy for generation quality. To ensure comprehensive comparison, report results under two established evaluation protocols (Full Retrieval and Random Sampling). As shown in Table 2 Pose-RFT achieves the best performance across most metrics. We attribute this success to our reinforcement fine-tuning with a semantic alignment reward, which effectively enhances the model's ability to capture fine-grained text-pose correspondence.

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4.3 ABLATION STUDIES AND DISCUSSIONS
Pose-Aware Encoder. We evaluate the effectiveness of the proposed pose-aware encoder, our spe- cialized visual module designed to capture fine-grained pose information from the input image. As shown in Figure 3] compared to relying solely on generic CLIP encoder, this specialized module leads to significantly higher spatial location reward score on the 3DPW dataset. This improvement establishes a more powerful SFT baseline for subsequent fine-tuning. We do observe, however, that this vision-centric module brings little benefit to the semantic reward in the text-to-pose task, an expected outcome as its features are less aligned with textual inputs.
Distributional Modeling. Next, we analyze the impact of modeling the 3D pose output as a probabilistic distribution rather than deterministic one-a key prerequisite for our sampling-based RFT approach. As shown in Table 3 introducing the distributional head (Baseline + Dist.) by itself yields only marginal changes performance compared to the de- terministic (Baseline). However, its crucial role is revealed when combined with reinforce- ment learning (Baseline + Dist. + RFT), where achieves the best performance. This syn- ergy demonstrates that distributional modeling critical enabler, facilitating more effective reward-driven exploration and learning.
Spotial
Score
Semantic Alignment Reward Score
0.14
th AFT
0.10
25
0.09
3.3
0.02
941
Original Model Pose-Amure Mudel
Original Pose-Amure
Figure 3: Ablation Study of Pose-RFT's Core Components. Both the Pose-Aware Encoder and Reinforcement Fine-tuning (RFT) contribute pos- itively, with RFT providing the most significant gains across both semantic and spatial rewards.
Effectiveness of Reinforcement Fine-tuning. With stronger architecture and probabilistic policy place, we now demonstrate the core contribution of our work. As shown in both Figure 3 and T applying reinforcement fine-tuning (+ RFT) provides the most significant performance gains across tasks and metrics. This consistently positive result validates our central hypothesis: shifting from supervised paradigm to reward-driven RFT paradigm is highly effective in enhanc- ing the alignment between language, vision, and 3D pose representations, successfully closing the alignment gap.
Table 3: Ablation study on distributional modeling (denoted as "Dist.") for 3D pose generation. Reconstruction and retrieval metrics reported the 3DPW and PoseScript-H2 datasets.
Method
Dist.
RFT
Image-to-Pose Generation
Text-to-Pose Generation mRecall T2P mRecall P2T
MPIPE 1 PA-MPIPE 4
Baseline
x
90.4
57.1
36.2
41.5
Baseline Dist.
/
x
91.4
59.2
37.4
42.0
Baseline Dist. + RFT
/
85.9
51.6
53.6
57.6
Necessity of Hybrid-Action RL (HyGRPO). Finally, we validate that specialized hybrid-action algorithm is essential for this success. We compare our proposed HyGRPO with GRPO, an algo- rithm designed for discrete-only action spaces. As illustrated in Figure GRPO fails improve the quality of the continuous 3D pose generation. contrast, HyGRPO, by jointly optimizing both discrete token and continuous pose parameters under the guidance of task-specific rewards, yields consistent and substantial improvements. This confirms that our novel HyGRPO algorithm is the key technical component that enables the success of RFT in this complex, hybrid-action task.
4.4 QUALITATIVE RESULTS
We provide qualitative results to visually validate our method on both tasks. For text-to-pose, Figure iillustrates the training progression, showing clear improvement in semantic alignment and struc- tural plausibility our reinforcement fine-tuning proceeds. For image-to- to-poe, Figure 6 demon- strates the superior spatial accuracy and realism of our Pose-RFT in a direct comparison against other leading ML.LM- methods challenging in-the-wild images.
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Spatial Reward
Semantic Reward
0.90
212
0.55
915
0.50
Reward
0.33
0.33
033
Training Steps
Stops
Figure 4: Comparison between GRPO and HyGRPO. Training reward curves for pose generation. The discrete-only GRPO fails to yield improvements, whereas our proposed HyGRPO achieves consistent gains, demonstrating that a hybrid-action approach is essential for optimizing continuous pose outputs.
The penson in dance pose, standing on their left leg. right
back wands. litted back. The two arms are outstretched to each side, with the right angled up.
He is bent forward with both knees bont and his face right above knee level looking down. Hlis right hanging down with his elbow bent and his hand in fromt between both of his legs the palm of hand facing towards his legs. His arm extended out his side about waist beight with the palm his hand faced up.
upper torso resting on the ground and bead facing straight up. with their lef leg mostly sticking strright up while their right leg hest the knee with foot faeing forwards.
penson lying
Training Steps
Figure 5: Training progression of text-to-pose generation. As reinforcement fine-tuning pro- gresses (left right), 3D poses generated from fixed text prompts exhibit increasingly improved semantic consistency and structural plausibility.
CONCLUSION
In this paper, we presented Pose-RFT, the first reinforcement fine-tuning framework designed to resolve the critical alignment gap MLLM- based 3D pose generation. We attribute this gap to the fundamental mismatch between the standard SFT paradigm and the inherent one- to-many nature of the pose generation task. To address this, Rose-RFT introduces paradigm shift to RFT, enabled by our novel HyGRPO al- gorithm, which is specifically designed han- dle the challenging discrete-continuous hybrid action space. Guided by task-specific rewards for spatial accuracy and semantic alignment, our framework directly optimizes for the true objectives of the task. Extensive experiments on multiple benchmarks demonstrate that Pose- RFT consistently outperforms existing pose- specific MLLMs. These findings validate that our hybrid-ae hybrid-action action reinforcement fine-tuning ap- proach an effective method for closing the alignment gap. More broadly, this work highlights the significant potential of RFT for unlock-
Image
Chathoo
PresRFT Unitaax
Figure 6: Qualitative comparison image-to- pose generation. Our Pose-RFT (bottom row) exhibits superior spatial accuracy and realism over baselines, especially capturing challeng- ing limb orientations and overall dynamies.

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ing the full capabilities of MLLMs complex, ambiguous generation tasks, paving the way for more aligned and controllable cemannccentric content generation.
ACKNOWLEDGMENTS
This work was supported in part by the National Key Research & Development Program (No. 20252DD013301),, the Chinese National Natural Science Foundation Projects (Nos. 62206280, 92570119), 19), the Beijing Natural Science Foundation (No. 4254100), the Beijing Advanced Inno- vation Center for Future Blockchain and Privacy Computing, the Young Scientists Fund of the State Key Laboratory of Multimodal Artificial Intelligence Systems (ES2P100113) 13), the Postdoctoral Fel- lowship Program and China Postdoctoral Science Foundation (Nos. 2024M764093,, BX20250485), and the InnoHK program.
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A DETAILS OF POSE-AWARE ENCODER AND THE VISUAL FUSION STRATEGY
This section provides additional implementation details of the Pose-Aware Encoder and the corre- sponding visual feature fusion strategy used in our framework.
Previous works (Liu et al. 2023 Feng et al. 2024) typically adopt the CLIP visual encoder (Radford et al. 2021) as the visual backbone. However, since CLIP is pretrained using glohal and coarse- grained supervision from image-caption pairs, struggles capture fine- fine-grained pose details. In contrast, pose estimation tasks require precise localization of human keypoints, encouraging the encoder to learn fine-grained, pose-aware representations. To address this limitation, we introduce pose-specific Vision Transformer (Goel al. 2023) pretrained on human pose estimation into the visual pipeline, as illustrated in Figure
Let fa denote the CLIP visual encoder and fo the pose-aware encoder. Given input image extract two sets of visual embeddings: Va = fx) E R Xd, and 8/ = (2) E RR h, xd., where Lu is the number of visual tokens, and da. d, are the respective emhedding dimensions. While UniPose (Li et al.) (025c) directly concatenates Va and fi along the channel dimension and applies a single linear projection W E R(d,+dx)xx this design can lead to patch-level misalignment due to the differing preprocessing pipelines of the two encoders, potentially degrading performance.
To preserve the individual strengths of each visual encoder, we propose to project ca and t individ- ually using two separate linear layers:
0 Wavay vis Wo6., W. E Rd.xd W, Rdxxd
(11)
The transformed features ry and eg are then used in a token-level fusion with language features during joint training. This design maintains the representational integrity of both visual encoders while aligning their output with the language model's embedding space.
B THEORETICAL DERIVATION OF THE HYGRPO OBJECTIVE
Our goal is to optimize hybrid policy To (a plq). where is discrete action (e.g., text sequence), and p continuous action (e.g., 3D human pose), both conditioned on the input q. We assume the policy factorizes as:
Te pl = =a(all) Te(plg,a).
(12)
where Fu is the discrete policy and n. is the continuous policy conditioned on the discrete output. To simplify the derivation, we temporarily exclude the clipping and KL regularization terms from the GRPO (Shao al. 2024) objective. These components are included in the final training objective but are omitted here for clarity. We begin with the simplified form of the GRPO objective
1 T
E (a
(0)
(13)
Here, r0) is the importance weight of the i-th sampled output, computed as the ratio between the current policy and the reference policy:
(0) To (a,, p:lg)
To (a(a))))
(p a;)
प (4;, Ps/g)
(re((aslg)
(14)
ra(a:19)
(pslg.a
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To effectively train the hybrid policy, decompose the surrogate loss into discrete and continu- ous components. This is motivated by the nature of our task design, where the rewards are defined separately for the discrete and continuous outputs: textual rewards Ra(q, evaluate the seman- tic correctness of the generated answer a. pose rewards Re(g: p) measures the plausibility and relevance of the generated pose conditioned both the question and answer.
Accordingly, we decompose the advantage estimate into discrete and continuous components:
Å(g. a, = F(g.a) a)
(q a.
(15)
discrette advantages
continucus advaninges
This decomposition does rely additive assumption reflects the fact that the discrete and continuous components are supervised by independent reward signals tailored to their modalities. Accordingly, compute two independent advantages from these separate rewards, using per-sample normalization within the candidate set:
shared reward function. Instead,
R mean (
R mean (Re)
F(g.a.)
A.(g,au.p))
(16)
std( (R
We now substitute this decomposition into the GRPO objective. To facilitate this, we first move the expectation over continuous action into an inner term:
E Bai
Ealo pece
(17)
=.(gga:))
We then analyze the inner term G(g, az) by substituting the advantage decomposition:
G(g, a:) = Ep: (re a. Fi a.) + (a.p
F(g, a.) Bp Ir a, p
(18)
- as) Ep a, p;) (,,a),,
Substituting G(g. az) back into the outer expectation, arrive at natural decomposition into two components:
E (a.) ~ (ra(aslg) ((lg, + (2 In az)r az p: A,) a, p.)]
Ja
Je
(19)
Although the discrete importance weight ra(alg) and the continuous policy Te (plq, share com- mon embedding space and are thus implicitly coupled through shared parameters, ra(alg) does not directly depend on the parameters of the continuous branch. practice, when generating valid con- tinuous 3D poses, the discrete answers are highly templated. Thus, ra(alg) g) can be treated as constant with respect to the optimization of the continuous component. Therefore, the continuous policy gradient is proportional to:
VaJe & (ol.) p;.))(lg.....))(..g a:,):
o,
(20)
Based on the decomposed gradient structure, apply PPO-style (Schulman al. 2017) clipping separately to the discrete and continuous components to stabilize training:
TNyGRPO =E =E (e..
imin(rafi, 1-c, 12c)
(21)
V
min(r:A,, clip(rr, 1- (c)) - (300)>3DDKK DDK
+
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Table Hyperparameter settings of pose-specific MLLM pretraining and reinforcement tine-tuning.
Hyperparameters
Pretraining
Reinforcement fine-tuning
Batch Size
80
16
Learning Rate
3e-4
le-6
Training Steps
10000
1000
Optimizer
AdamW
Adam W (0.9, 0.95)
Adam
(0.9, 0.95)
LR Schedule
Cosine
Cosine
Computing Resources
NVIDIA A100 (40GB)
NVIDIA A800 (80GB)
where is the total number of sampled candidates per input, and V S G is the number of can- didates with valid continuous outputs. This objective enables separate, stable, and reward-aligned optimization discrete and continuous policy branches within unified reinforcement learning framework.
C ALGORITHM
Algorithm Hybrid Action Space Group Relative Policy Optimization
1: Input: Initial policy model Tinit (a pretrained pose-specifc MLLM); reward models Ri: task dataset D; hyperparameters
2: Output: policy model m
3: Initialize Te <- Tinit Teef f- Tnit
4: for iteration = 1,..., N do
5: Sample batch DD from D
6: Generate outputs {a, p ~ TT(- q) for each question & E DD
7:
Compute rewards { for each (a,. by running R.
8: Compute A, for (a,, via group relative advantage estimation
9: Update we by maximizing HyGRPO objective Eq.
10: end for
D EXPERIMENTAL DETAILS
The detailed hyperparameter settings for both Pose-specific MILM pretraining and reinforcement fine-tuning provided in Table In the pretraining stage, we focus on adapting the base LLaVA (Liu et al. 2023) model to 3D pose tasks, while the reinforcement fine-tuning stage further optimizes the policy behavior.
E MORE QUALITATIVE RESULTS: TEXT-TO-POSE
In Figure we present qualitative results of Pose-RFT applied human-written prompts from PoseScript
(Delmas et al. 2022).
F MORE QUALITATIVE RESULTS: VIDEO-TO-POSE
In Figure 8 present qualitative results of Pose-RFT applied to in-the-will videos. As frame-
based model. Pose-RFT processes each frame independently, without the integration of any temporal smoothing post-processing module.
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"hee person has thenee limbs on the ground,
Somsome is bent over completel and is touching both of their feet with their hands while looking down.
only the left log is raised with the knee line with thee bock..
The person standing the left foot, with the right log stretcheul forward perfectly horinontally: The right along the bodly wisle the left arm bent furward, with the hand raisod alihtlly higher than the ellows.
The person their ungled diagonally downwords and their right held roughly level with their shows while their head leans the left. The left crossed front of the other.
elightly bent
Figure 7: Pose-Ree RFT results on human human-written prompts from PoseScript DDelmas et al. 2022
Figure 8: Pose-RFT results on in-the-wild videos.
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Table 5: Ablation study reward components. We report the performance impact of removing individual rewards during RL. fine-tuning.
Image-to-Pose (3DPW)
Text-to-Pose (PoseScript-H2)
Method
MPJPE
PA-MPIPE +
R T2P
RP2T
Baseline (no RL)
91.4
59.2
37.4
42.0
w/o Joint Location Reward
108.7
73.5
55.9
60.3
w/o Semantic Alignment Reward
84.0
51.3
35.2
40.8
w/o Format Reward
131.9
80.6
28.3
34.4
w/o Text Emb. Sim. Reward
89.8
58.7
42.3
46.5
Rose-RFT (Full Model)
85.9
51.6
53.6
57.6
Table 6: Cross-Model Evaluation using PoseEmbroidee Retrieval Model.
Text-to-Pose (11
Pose-to- Text (It (
Method
R@5
R@ 10
R@
R@5
R@10
R@20
Baseline
47.7
78.3
86.1
46.2
80.3
88.7
Pose-RFT (Ours)
55.2
85.9
92.1
51.6
85.2
92.0
G ABLATION ON REWARD COMPONENT
To validate the efficacy of our proposed reward design, we performed an ablation study by system- atically excluding individual reward terms during RL. fine- fine-tuning. Specifically, we zero-masked the target reward signal while maintaining the multi-task training pipeline to strictly isolate the contribu- tion each component. As shown 5 the removal any single reward leads to performance degradation in its corresponding domain. For instance, excluding the Joint Location Reward signifi- cantly impairs geometric accuracy (PA-MPJPE 1 21. 21.9mm), while removing the Semantic Alignment Reward deteriorates text-pose consistency. Notably, the Format Reward proves critical for overall stability; its absence results catastrophic failure across both modalities, confirming that structured output constraints prerequisite for effective optimization.
H CROSS GROSS-RETRIEVER EVALUATION FOR TEXT-TO-POSE
To further validate the generalization capability of our approach and ensure that the learned semantic alignment is intrinsic rather than specific to the reward model's feature space, we conducted cross- retriever evaluation using an independent pose retrieval framework. Specifically, we employed the
retrieval model from PoseEmbroider (Delmas et al. 2024) an external evaluator in Table 6
While PoseEdbroider shares a similar encoder architecture (VPoser + Transformer) with our reward model (PoseScript), it serves robust out-of- of-distribution test due to two fundamental differences: Data Distribution Shift: PoseEmbroider was trained on BEDLAM-Scripp (Black et al., 2023). dataset derived from high-fidelity synthetic avatars. This represents significant domain shift from AMASS (Mahmood al., (2019) data used to train our reward model. Distinct Training Frame- work: Unlike the joint embedding approach of PoseScript, PoseEmbroider utilizes distinct multi- modal embroidery framework with uni-modal contrastive objectives.
I LIMITATIONS
While our method represents a promising step toward reinforcement learning- baseid 3D human pose generation, it has several limitations. First, its effectiveness is inherently constrained by the quality of the reward functions. Designing reliable and semantically meaningful reward signals for pose generation remains challenging problem, especially when capturing nuanced human preferences
18

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Published as a conference paper at ICLR 2026
such plausibility, naturalness, or contextual relevance. Inaccurate or incomplete reward feedback may misguide policy optimization, leading to suboptimal or unnatural poses.
Second, our framework relies sampling multiple candidate responses per input to perform group- wise reward normalization. Although this design improves training stability in hybrid action spaces, it introduces non-negligible computational overhead, which may limit scalability when applied to larger models or datasets.
J THE USAGE OF LARGE LANGUAGE MODELS (LLMS)
We used a Large Language Model (LLM) as general- purpose writing assistant to improve the clarity, grammar, and style of the manuscript. The LLM provided suggestions for sentence phrasing and readability, but all content, ideas, and scientific claims in this paper are the sole responsibility of the authors.
19


Reviews:
[{"review_id": "kjPw6dBnb1", "reviewer": "Reviewer_eur6", "summary": "This paper proposes Pose-RFT, a reinforcement fine-tuning (RFT) framework for 3D human pose generation from multimodal inputs such as text and images. This work aims to overcome a fundamental limitation in existing pose-specific MLLMs, which is the semantic–spatial alignment gap that arises from their reliance on supervised fine-tuning (SFT) with regression-based objectives.  \nInstead of directly regressing SMPL parameters via supervised learning, Pose-RFT reformulates the task as a hybrid action space reinforcement learning problem, where the model simultaneously generates discrete language tokens and continuous 3D pose parameters. To achieve stable and efficient optimization in this mixed action space, they introduce HyGRPO, a hybrid reinforcement learning algorithm that applies group-wise reward normalization across sampled responses. Furthermore, the framework incorporates four task-specific reward functions targeting spatial accuracy (for image-to-pose), semantic alignment (for text-to-pose), output format correctness, and embedding-level consistency.\nExperiments are conducted on 3DPW, Human3.6M, and RPE for evaluating 3D HPS; on PoseScript for test-topose generation, demonstrating promising performance gains compared with prior SFT-based pose-specific MLLMs, validating the effectiveness of the reinforcement fine-tuning paradigm in achieving both semantic and spatial fidelity in 3D pose generation.", "questions": "1. How efficient are the model's inference and training (compared to the supervised training)? This information helps readers understand its advantages and disadvantages.\n2. How well the model understands complex textual expressions—is this one of the factors limiting the model's performance?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper makes a clear paradigm shift from supervised regression to reward-based reinforcement optimization for multimodal 3D pose generation, which is a timely direction. \n2. They adopt HyGRPO to addresses the challenge of jointly optimizing discrete and continuous outputs, which seems to be a reasonable and effective solution. .  \n3. The integration of task-specific rewards provides a well-structured mechanism to guide policy learning toward both geometric and semantic alignment. \n4. Experimental results show solid quantitative improvements over multiple baselines, reinforcing the proposed method’s effectiveness and generality. The ablation studies of HyGRPO show its positive effects on the performance.", "weaknesses": "1. Limited qualitative results. Only a few images in Figure 5 and Figure 6 presented in this paper. Providing more visualizations would help readers gain a more intuitive understanding of the performance of the proposed model. \n2. Reward sensitivity. Since multiple reward terms are combined, it would be helpful to include a discussion or visualization of how different reward weights affect learning dynamics. \n3. Computational overhead. Reinforcement fine-tuning can be computationally expensive; providing efficiency comparisons or scalability analysis would strengthen the practical relevance.", "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": "de6kTPJ1Ix", "reviewer": "Reviewer_rGEb", "summary": "Pose-RFT introduces a RL fine-tuning framework for MLLM-based 3D pose generation. This framework, HyGRPO, utilizes a mixed optimization strategy to optimize both discrete tokens (from the LLM) and continuous motion (SMPL params).", "questions": "How well does this method do on videos? It would be super interesting to see how stable the model predictions would be in a temporal setting.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Enabling LLMs to estimate / generate poses is a highly relevant task and utilizing a RL-based framework to optimize this is a reasonable strategy.", "weaknesses": "My major concern is that one of the main premises of this work, that Pose-RFT is required for better semantic and spatial alignment of HMR, is misleading. The statement that the “reliance on objectives like SMPL parameter regression creates a … alignment gap, … [to] … achieve the required semantic and spatial fidelity” (l016-019) is inaccurate or unclear.\nArguably, the simplified assumptions about the context (3d world, pose, camera) are the reason for “poor” performance HMR. On one hand, generative models like Score-HMR [1] have been utilized successfully and the authors should discuss this line of work. On the other hand, more accurate modeling of the context, i.e. like has been done in CameraHMR [2] solves the problem and simply egressing the SMPL parameters actually outperforms all other methods.\n\nOne of the main contributions of the paper is HyGRPO which is an optimization algorithm to optimize both discrete and continuous outputs. This is necessary as the authors note themselves, because MLLMs operate in discrete space but poses are in continuous space. However, there is a body of work that successfully learns discrete tokenization for human poses, for example TokenHMR. I wonder if the authors could leverage the TokenHMR tokenizer instead of having to jointly optimize discrete and cont’ signals - at least as a baseline.\n\nThe authors should also evaluate on EMDB [3], which provides more accurate gt poses.\n\n\n[1] ScoreHMR: Score-Guided Diffusion for 3D Human Recovery; CVPR 2024\n\n[2] CameraHMR: Aligning People with Perspective; 3DV 2025\n\n[3] EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild; ICCV 2023", "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": "lBswUmsqBr", "reviewer": "Reviewer_kH8o", "summary": "This paper aims to address the alignment gap problem in current 3D human pose generation methods. The authors point out that existing methods primarily rely on Supervised Fine-Tuning, learning a deterministic mapping from input to a single ground truth pose through regression and other methods. However, 3D pose generation tasks (whether text-to-pose or image-to-pose) inherently possess a \"one-to-many\" ambiguity, which the SFT paradigm struggles to handle. \n\nTo address this issue, the paper proposes the Pose-RFT framework, using reinforcement learning fine-tuning. The core technical challenge lies in handling a hybrid action space, requiring simultaneous optimization of discrete text outputs and continuous 3D pose parameters. To this end, the authors design a novel online reinforcement learning algorithm called **HyGRPO** (Hybrid Action Space Group Relative Policy Optimization). This algorithm achieves stable optimization of the hybrid policy by grouping and normalizing the rewards of a sampled set of candidate outputs.\n\nFurthermore, the paper designs a set of task-specific reward functions to guide spatial alignment for image-to-pose tasks and semantic consistency for text-to-pose tasks, respectively. Extensive experiments on multiple 3D pose generation benchmarks demonstrate that Pose-RFT achieves significant performance improvements over existing pose-specific MLLMs.", "questions": "1. On standard benchmarks such as Human3.6M and 3DPW, your approach exhibits a significant performance gap compared to domain expert models. Could you elaborate on the reasons for this gap and articulate it as a fundamental trade-off between using a general-purpose MLLM architecture and a specialized architecture?\n\n2. Evaluation of text-to-gesture tasks relies on the same retrieval model that provides the semantic reward signal, which can lead to a \"test-taking\" problem. How do you view this potential methodological limitation? Have you considered employing alternative evaluation strategies?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- In the context of MLLM, the authors define pattern collapse in generative models as \"alignment gap”. Furthermore, the authors demonstrate why a shift from supervised imitation to a reward-driven optimization paradigm is necessary.  \n- The authors demonstrate that RL can not only be used to align a model's internal understanding, but also to directly guide the model to generate complex, structured non-textual data.  \n- The authors propose the HyGRPO algorithm to provide independent and targeted feedback signals for different output heads of the model, which is a simple and logically consistent approach.", "weaknesses": "- Pose-RFT achieved PA-MPJPE values of 44.5 mm and 85.9 mm on Human 3.6M and 3DPW, respectively. The paper mentions a \"performance gap\" compared to traditional expert models, but quickly shifts its focus to its state-of-the-art (SOTA) performance on the RPE task. However, this performance gap is not insignificant, but rather quite substantial. Current SOTA expert models already have PA-MPJPE values far below this value on Human 3.6M, for example, 29.1 in ([CVPR 2023 paper](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Learning_Analytical_Posterior_Probability_for_Human_Mesh_Recovery_CVPR_2023_paper.html)) and 29.4 in ([Springer link](https://link.springer.com/chapter/10.1007/978-3-031-72640-8_27)). This may be due to differences in assessment protocols, which the authors need to clarify, but it undoubtedly underestimates the true gap. The authors should consider repositioning the paper's contribution as \"bringing new interactive and inference capabilities to pose estimation,\" rather than merely competing on traditional metrics.  \n\n- In the text-to-pose task, the authors used a pre-trained text-pose retrieval model to define the semantic alignment reward. However, in the evaluation phase, they also used the same retrieval model (derived from the PoseScript paper) to calculate the Recall@K metric. This design leads to a methodological loop: the RFT process directly optimizes the model to generate poses that score high on the similarity metric of the retrieval model, and then uses the same metric to evaluate its performance. This can lead to overfitting to the evaluation metric, where the generated poses may not appear more reasonable or accurate to human observers, but are simply better at matching the feature space of the evaluation tool. This is a serious limitation that undermines the credibility of the results for the text-to-pose generation task.  \n\n- HyGRPO appears to be an adaptation of the GRPO algorithm in a hybrid action space. While this application is novel, its core algorithmic components likely have stronger precedents in the broader hybrid RL literature than the paper acknowledges. The paper should more clearly compare its algorithm with these existing works to clarify its exact novelty.  \n\n- The paper proposes four independent reward functions, but does not discuss the weight balance and potential conflicts among them.", "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": "xQ6yWby2yG", "reviewer": "Reviewer_6Rpp", "summary": "The paper addresses the alignment gap in text/image-to-pose generation, where the one-to-many nature of 3D poses makes supervised fine-tuning (SFT) suboptimal. The authors propose Pose-RFT, framing the task as hybrid-action reinforcement learning and introducing HyGRPO for joint optimization over discrete (text) and continuous (pose) spaces. HyGRPO normalizes group-wise rewards and decomposes the advantage into discrete (language) and continuous (pose) components. Four task-specific reward functions are designed: spatial, semantic, format, and text-similarity.", "questions": "1. Have the authors conducted reward ablations? If not, please clarify which components are most influential.\n2. Could you evaluate under a different embedding/retrieval model to test generalization?\n3. How do key hyperparameters (group size G, continuous sample count V) affect performance, efficiency, and stability?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "*Originality:* The motivation is clear and well-grounded. Applying reinforcement fine-tuning to 3D pose generation is novel and well-justified given the one-to-many mapping nature.\n\n*Quality:* Extensive experiments across multiple datasets (3DPW, H36M, PoseScript) demonstrate SOTA performance against MLLM baselines. Ablations are comprehensive.\n\n*Clarity:* Writing and figures are clear and intuitive.\n\n*Significance:* Introducing RFT into multimodal 3D pose generation is timely and impactful for the community.", "weaknesses": "1. **Lack of ablation on reward components.**\n   Although the paper emphasizes the importance of the four reward functions, it provides no analysis isolating their individual contributions. Section 4.3 only examines Pose-aware Encoder, distribution modeling, and RFT as a whole. Hence, it remains unclear which reward(s) primarily drive the improvements—semantic alignment, spatial accuracy, or the mere presence of RL signal.\n\n2. **Reward–evaluation entanglement.**\n    Some reward terms use the same embedding models as the evaluation metrics (e.g., BGE-M3, PoseScript retriever), creating potential bias or “reward hacking.” Cross-evaluator validation would strengthen claims of generality.\n\n3. **Computational overhead.**\n   HyGRPO requires sampling multiple candidates and performing group normalization. While the appendix mentions resource cost, there are no efficiency statistics or scaling curves quantifying the trade-off.", "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)