AutoCompact trains agents to compact context themselves, +9.2 on SWE-bench Verified
omarsar0 · x · 2026-10-04
AutoCompact trains agents to natively decide when to compact context, what working state to keep, and how to resume. A judge reviews and corrects the base agent's compaction decisions, corrected trajectories feed SFT, then RL with task-success rewards trains coding and compaction jointly. Pass rates improve by 9.2 points on SWE-bench Verified and 5.0 on SWE-PolyBench Verified — gains that hold even with a never-overflowing 256K window. Part of a rising trend (AutoHarness, AutoContext, Meta's context-management paper) of models natively absorbing harness duties.
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