GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal
  Reasoning
Authors: name: Yi Chen, name: Yuying Ge, name: Rui Wang, name: Yixiao Ge, name: Junhao Cheng, name: Ying Shan, name: Xihui Liu

Recent reinforcement learning approaches, such as outcome-supervised GRPO,
have advanced Chain-of-Thought reasoning in large language models (LLMs), yet
their adaptation to multimodal LLMs (MLLMs) is unexplored. To address the lack
of rigorous evaluation for MLLM post-training methods, we introduce
SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced
perception and reasoning. It offers a large training set and evaluates
generalization across three escalating challenges: in-distribution,
cross-environment, and cross-environment-task scenarios. Using SEED-Bench-R1,
we find that standard GRPO, while improving answer accuracy, often reduces
logical coherence between reasoning steps and answers, with only a 57.9%
consistency rate. This stems from reward signals focusing solely on final
answers, encouraging shortcuts, and strict KL penalties limiting exploration.To
address this, we propose GRPO-CARE, a consistency-aware RL framework optimizing
both answer correctness and reasoning coherence without explicit supervision.
GRPO-CARE introduces a two-tiered reward: (1) a base reward for answer
correctness, and (2) an adaptive consistency bonus, computed by comparing the
model's reasoning-to-answer likelihood (via a slowly-evolving reference model)
against group peers.This dual mechanism amplifies rewards for reasoning paths
that are both correct and logically consistent. Replacing KL penalties with
this adaptive bonus, GRPO-CARE outperforms standard GRPO on SEED-Bench-R1,
achieving a 6.7% performance gain on the hardest evaluation level and a 24.5%
improvement in consistency. It also shows strong transferability, improving
model performance across diverse video understanding benchmarks. Our work
contributes a systematically designed benchmark and a generalizable
post-training framework, advancing the development of more interpretable and
robust MLLMs.