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GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR

This paper introduces GIST, a two-level data selection method for offline reinforcement learning with verifiable rewards (RLVR). It derives a factorization of the KL divergence between the current policy and the Gibbs-optimal target, identifying which responses and tokens are most informative. GIST selects correct responses on-policy and incorrect responses near the policy's mode, and supervises only the tokens where the mismatch lies. Experiments show accuracy gains across GSM8K, MATH-500, and MBPP for three models, with 66.6% fewer supervised tokens in a controlled comparison.

arxiv1d agoScore 78
Reinforcement LearningData SelectionToken Efficiency
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Microsoft and UC Berkeley Release TokenEconomizer to Cut Coding Agent Costs by Up to 30.9%

Microsoft Research and UC Berkeley researchers released TokenEconomizer, an Apache 2.0 open-source cost controller that plugs into Claude Code, Codex, GitHub Copilot CLI and Pi without modifying model weights or agent code. It learns per-repository policies from past traces through three mechanisms: micro-skills, tool pruning and failure-loop intervention. Across 277 SWE-Bench tasks and five agent-model settings, it reduced mean cost by 18.4% to 30.9%, and it was cheaper in all 30 repository-setting cells. Pass@1 improved in four of five settings and fell 1.0 point on Claude Code with Opus 4.8.

Why it matters: It gives teams a concrete, installable way to lower coding-agent token spend, backed by per-setting SWE-Bench numbers and an honest account of where savings are smaller.

MarkTechPostOct 31, 2026Score 87
Microsoft Researchcoding agentstoken efficiency