Reinforcement Learning Framework Improves VLM Perception-Reasoning Synergy

A new reinforcement learning framework aims to resolve the trade-off between perception and reasoning in Vision-Language Models (VLMs). The paper, published on arXiv, argues that the root cause of VLM failures is an ambiguity in modality credit assignment: whether errors stem from flawed perception ("bad seeing") or flawed logic ("bad thinking"). The proposed framework improves perception-reasoning synergy by explicitly rewarding perception fidelity, avoiding the "seesaw effect" seen in prior approaches that rely on static textual reasoning or complex agentic workflows. The method decomposes the credit assignment problem, enabling more efficient and robust VLM performance without heavy compute or engineering burden.