TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning
arXiv:2605.28699v1 Announce Type: new Abstract: Large language models increasingly rely on either reinforcement learning or multi-agent prompting to improve reasoning, yet these two paradigms remain difficult to combine. Directly applying single-agent reinforcement learning to multi-turn multi-agent systems faces following dilemmas: i) Sparse rewards, role-level free-riding and excessive training overhead. ii) Agents only imitate to collaborate. iii) Fixed collaboration protocol falls into oscillating local optimum. We introduce TRACER, a turn-level reinforcement framework for cooperative mult
The increasing complexity of multi-agent LLM systems and the limitations of current reinforcement learning approaches necessitate novel frameworks to enhance cooperative reasoning.
Improving multi-LLM cooperation is crucial for developing more robust, autonomous, and capable AI systems that can tackle complex problems currently beyond single-agent capabilities.
This research introduces a novel reinforcement learning framework that specifically addresses the challenges of sparse rewards, free-riding, and oscillations in multi-agent LLM cooperation, potentially enabling more effective collaboration.
- · AI research labs
- · Developers of multi-agent systems
- · SaaS companies leveraging LLMs
- · Inefficient multi-agent LLM frameworks
- · Systems relying on fixed collaboration protocols
More sophisticated multi-LLM applications become feasible.
Increased efficiency and autonomy in complex white-collar workflows currently involving human coordination.
Accelerated development of general-purpose AI agents capable of addressing broader societal challenges through emergent cooperative intelligence.
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Read at arXiv cs.AI