TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination

[2605.15207] TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination

[Submitted on 1 May 2026]

Title:TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination

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Abstract:Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mode in sequential fine-tuning of shared-context teams: updating one agent shifts the team's context distribution, and when subsequent updates are evaluated on cached rollouts, this mismatch compounds. We formalize this as the compounding occupancy shift and prove that stale-occupancy evaluation incurs a penalty that scales quadratically with the number of agents. In contrast, intermediate-occupancy evaluation reduces this to linear scaling. We propose TeamTR, a trust-region framework that resamples trajectories after each component update and enforces per-agent divergence control, yielding rigorous per-update and per-stage improvement lower bounds. Experiments show that TeamTR outperforms single-agent and sequential baselines with 7.1% on average, mitigates coordination regressions, and supports plug-and-play component replacement. Code is available at this https URL.

Comments: 9pages, Accepted at ICML2026

Subjects:

Machine Learning (cs.LG); Multiagent Systems (cs.MA)

Cite as: arXiv:2605.15207 [cs.LG]

(or arXiv:2605.15207v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.15207

arXiv-issued DOI via DataCite

Submission history

From: Yi Xie [view email] [v1] Fri, 1 May 2026 23:42:57 UTC (1,124 KB)

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