MRC-MMAgent: Reinforcement Learning for Multi-Role Collaboration in Multimodal Tool-Using Agents

Key points

It places solving, inspection, and continuation roles in one executable agent loop, where the inspector judges evidential support of the answer and either finalizes or resumes solving.
It combines task success, interaction validity, and successful correction into one trajectory reward with weights (λacc, λfmt, λcorr) = (0.7, 0.1, 0.2).
Full-trajectory GRPO propagates the combined reward to every role decision, avoiding the post-hoc role-attribution bias that role-selective masking introduces.
Using only the MMSearch-R1 training set, it improves upon the base model by 11.1 points and reaches an average of 68.1 with the 8B model, leading same-scale systems.
On image reasoning benchmarks it achieves the best V* Bench score of 93.2 and HR-Bench 8K score of 78.5, gaining +6.8 on V* Bench and +3.9 on HR-Bench 8K over the base model.
Without inspector training, 98.3% of trajectories are marked for continuation causing unnecessary revisions; training reduces the continue rate to 60.4% and raises accuracy from 62.7 to 64.0.

External research summaries. These are not HDATF publications or measured product results.