CompactionRL

CompactionRL trains long-horizon agents with context compaction via reinforcement learning

Researchers propose CompactionRL, a reinforcement learning strategy that jointly optimizes task execution and context summarization to enable LLM agents to operate beyond finite context windows. The method uses token-level loss normalization and cross-trajectory generalized advantage estimation to learn from compacted long-horizon trajectories. Applied to open GLM models, CompactionRL achieves 66.8% Pass@1 on SWE-bench Verified with GLM-4.5-Air (106B-A30B), a 7.0-point absolute gain, and has been incorporated into the training pipeline for GLM-5.2 (750B-A40B).