CPPO: Contrastive Perception for Vision Language Policy Optimization
We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision-language models (VLMs). While reinforcement learning (RL) has advanced reasoning in language models, extending it to multimodal reasoning requires improving both the perception and reasoning aspects. Prior works tackle this challenge mainly with explicit perception rewards, but disentangling perception tokens from reasoning tokens is difficult, requiring extra LLMs, ground-truth data, forced separation of perception from reasoning by policy model, or applying rewards indiscriminately to all output tokens. CPPO addresses this problem by detecting perception tokens via entropy shifts in the model outputs under perturbed input images. CPPO then extends the RL objective function with a Contrastive Perception Loss (CPL) that enforces consistency under information-preserving perturbations and sensitivity under information-removing ones. Experiments show that CPPO surpasses previous perception-rewarding methods, while avoiding extra models, making training more efficient and scalable.
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Ahmad Rezaei
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2026-01-07T09:26:22.766Z
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Ahmad Rezaei
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Mohsen Gholami
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2026-01-08T08:33:19.178Z
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Mohsen Gholami
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mgholami
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Saeed Ranjbar Alvar
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2026-01-07T09:26:24.914Z
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Saeed Ranjbar Alvar
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Kevin Cannons
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Mohammad Asiful Hossain
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Zhou Weimin
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Shunbo Zhou
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Yong Zhang
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2026-03-06T13:58:16.960Z
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Yong Zhang
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Mohammad Akbari
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2026-01-07T09:26:18.199Z
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Mohammad Akbari
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CPPO: Contrastive Perception for Vision Language Policy Optimization
We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision-language models (VLMs). While reinforcement learning (RL) has advanced reasoning in language models, extending it to multimodal reasoning requires improving both the perception and reasoning aspects. Prior works tackle this challenge mainly with explicit perception rewards, but disentangling perception tokens from reasoning tokens is difficult, requiring extra LLMs, ground-truth data, forced separation of perception from reasoning by policy model, or applying rewards indiscriminately to all output tokens. CPPO addresses this problem by detecting perception tokens via entropy shifts in the model outputs under perturbed input images. CPPO then extends the RL objective function with a Contrastive Perception Loss (CPL) that enforces consistency under information-preserving perturbations and sensitivity under information-removing ones. Experiments show that CPPO surpasses previous perception-rewarding methods, while avoiding extra models, making training more efficient and scalable.
CPPO improves vision-language model fine-tuning by detecting perception tokens through entropy shifts and using contrastive perception loss to enhance multimodal reasoning efficiency.
Contrastive Perception Policy Optimization vision-language models reinforcement learning perception tokens reasoning tokens entropy shifts Contrastive Perception Loss information-preserving perturbations information-removing perturbations