CompactionRL makes context compaction a trained policy, +7 on SWE-bench.

Tsinghua's CompactionRL folds summarization into RL rollout collection so the agent learns what to keep when it compresses, optimizing summary and task under one reward. Under fixed 64k–80k windows it adds +5.5 to +7.0 on SWE-bench Verified and +3 to +6.8 on Terminal-Bench versus heuristic compaction. If you run long-horizon coding agents, stop treating "summarize the last N turns" as a fixed prompt. The compression step is a tunable component, and the gap between trained and heuristic is bigger than most model upgrades.