AutoCompact trains coding agents to decide when to compact context
AutoCompact uses a judge to correct compaction decisions, then applies supervised fine-tuning and reinforcement learning with task-success rewards. Reported pass rates rise by 9.2 points on SWE-bench Verified and 5.0 points on SWE-PolyBench Verified, including when a 256K window never overflows.