CoSIR: Training LLMs for multi-agent collaboration with verifiable rewards
An arXiv paper posted on 13 Oct 2026, "Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards," introduces CoSIR (Collaboration via Surprisal-based Information Rewards), a post-training approach that uses a simple, verifiable environment to teach collaboration behaviors meant to generalize to more complex, unseen tasks. Its Bayesian evaluator scores shared information by the reduction in team surprisal — the sum of negative log probabilities assigned to the correct option given each agent's available evidence — scores requests by the potential reduction in the same quantity if the requested information were truthfully provided, and adds separate rewards for using received information in private choices and calculations. The authors post-train Qwen3-4B, Qwen3-8B and Gemma-3-4B-it through self-play and test transfer to seven unseen natural-language collaboration tasks: CoSIR achieves the highest aggregate score while remaining competitive on general tasks, and behavioral analyses show more disclosure of private evidence and fewer judged failures to use teammates' input. The paper runs 23 pages.
It offers a verifiable-reward recipe for training collaborative behaviors such as sharing evidence, asking for missing information and using teammates' input, a direction relevant to multi-agent LLM systems.