PhyCritic: Multimodal Critic Models for Physical AI
With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise preferences, numerical scores, and explanatory justifications for assessing model-generated responses. However, existing critics are primarily trained in general visual domains such as captioning or image question answering, leaving physical AI tasks involving perception, causal reasoning, and planning largely underexplored. We introduce PhyCritic, a multimodal critic model optimized for physical AI through a two-stage RLVR pipeline: a physical skill warmup stage that enhances physically oriented perception and reasoning, followed by self-referential critic finetuning, where the critic generates its own prediction as an internal reference before judging candidate responses, improving judgment stability and physical correctness. Across both physical and general-purpose multimodal judge benchmarks, PhyCritic achieves strong performance gains over open-source baselines and, when applied as a policy model, further improves perception and reasoning in physically grounded tasks.
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Tianyi Xiong
claimed_verified
2026-02-12T13:28:23.141Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6570977f87a92b76922c9950/AQGto1w6ugBvH2yCV46YU.jpeg
Tianyi Xiong
txiong23
user
txiong23
698d486865c0d15a6d162163
Shihao Wang
698d486865c0d15a6d162164
Guilin Liu
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Yi Dong
698d486865c0d15a6d162166
Ming Li
698d486865c0d15a6d162167
Heng Huang
698d486865c0d15a6d162168
Jan Kautz
698d486865c0d15a6d162169
Zhiding Yu
claimed_verified
2026-02-12T13:28:25.160Z
66c8037c737ba92ae3fe0322
https://cdn-avatars.huggingface.co/v1/production/uploads/66c8037c737ba92ae3fe0322/WR_Yh5DWOVVh7IFlF24NM.jpeg
Zhiding Yu
Zhiding
user
Zhiding
PhyCritic: Multimodal Critic Models for Physical AI
With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise preferences, numerical scores, and explanatory justifications for assessing model-generated responses. However, existing critics are primarily trained in general visual domains such as captioning or image question answering, leaving physical AI tasks involving perception, causal reasoning, and planning largely underexplored. We introduce PhyCritic, a multimodal critic model optimized for physical AI through a two-stage RLVR pipeline: a physical skill warmup stage that enhances physically oriented perception and reasoning, followed by self-referential critic finetuning, where the critic generates its own prediction as an internal reference before judging candidate responses, improving judgment stability and physical correctness. Across both physical and general-purpose multimodal judge benchmarks, PhyCritic achieves strong performance gains over open-source baselines and, when applied as a policy model, further improves perception and reasoning in physically grounded tasks.
PhyCritic is a multimodal critic model designed for physical AI tasks through a two-stage RLVR pipeline that enhances perception and reasoning capabilities.
multimodal models physical AI RLVR pipeline physical skill warmup stage self-referential critic finetuning perception reasoning policy model