Measure First, Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
arXiv:2610.28785v1 Announce Type: new Abstract: Training tool-using language models requires costly interaction, yet identical rewards within a rollout group provide no group-relative learning signal. We use reward disagreement to guide harness evaluation and the allocation of rollouts for policy training in Model Context Protocol (MCP) environments. We first introduce Discord, which tests harness edits using exact paired inference and decision-preserving early stopping: only disagreeing paired outcomes favor one harness over another. This insight motivates prospective allocation in BRACE (Ban
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