ContextPilot uses RL to teach agents proactive context management, 69.4 vs 45.9
rohanpaul_ai · x · 2026-09-03
The ContextPilot paper argues long-running agents don't just need bigger context windows — they need to learn what deserves to stay in context.
- ContextPilot lets agents plan and execute memory saves, summarization, compression and removal of old context; fine-grained RL trains which context decisions actually help
- ContextPilot-8B-RL (built on Qwen3-8B) averaged 69.40 across 4 long-context benchmarks vs 45.93 for the same model with a plain 128K window
- On BrowseComp its context stayed around 8K–10K tokens per turn while WebExplorer-8B grew toward 30K
- Recommendation: stop treating full conversation history as memory; give agents a smaller working context they actively manage
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