ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL
- Published
- Source
- arXiv
- Paper number
- 1048
- Field
- LLMs / NLP
- arXiv ID
- 2608.28476
Key points
- Adding planning, long-term memory, and offloading to the tool set raised BrowseComp+ accuracy from 63.49% to 80.96%.
- Reinforcement learning delivered a 5.34-point gain on the more difficult BrowseComp+ benchmark. The longer and harder the task, the greater the effect.
- The baseline agent's input length grew linearly with each turn to 30K tokens, whereas ContextPilot stabilized at 8–10K tokens.
- Reinforcement learning also reduced tool-use mistakes. Memory and offloading tool failures, which were frequent early on, decreased substantially during training.
- It showed consistent improvements across different base models, including Qwen3-8B/14B and Gemma4-E4B.
Paper links
External research summaries. These are not HDATF publications or measured product results.