From History to State: Constant-Context Skill Learning for LLM Agents
In recent years, large language model (LLM) agents have gained traction in various applications, particularly as personal assistants capable of operating browsers, files, and tools. However, the deployment of these agents is challenged by a critical dilemma: the privacy-cost-capability tension. Cloud-based models excel in executing multi-step workflows but often expose sensitive intermediate context to external APIs. On the other hand, local models prioritize privacy yet struggle with reliability. This dichotomy raises pressing questions about how to balance these competing needs while enhancing the performance of LLM agents.
A new research paper titled “Constant-Context Skill Learning for LLM Agents,” available on arXiv as 2605.05413v1, addresses this issue head-on. The authors propose a novel approach known as constant-context skill learning, designed to optimize recurring workflows in LLM agents. This innovative method not only seeks to enhance the agents’ capabilities but also aims to do so without compromising user privacy.
The Constant-Context Skill Learning Framework
The core of this framework is a context-to-weights mechanism that facilitates the learning of reusable procedures within lightweight task-family modules. This allows agents to operate more efficiently by reducing reliance on lengthy skill prompts and extensive histories, which can accumulate significant costs.
Key features of constant-context skill learning include:
- Compact State Block: The framework utilizes a compact state block that focuses solely on the current observation. This design minimizes the need for long historical prompts while maintaining necessary contextual information.
- Deterministic Tracker: A deterministic tracker is employed to render the state block based on task progress. This tracker also supplies aligned subgoal rewards, enabling the learning process to be more focused and effective.
- Step-Level Supervised Fine-Tuning (SFT): Each module can be trained using step-level SFT, which allows for a more granular approach to learning, ultimately enhancing the agent’s performance.
- Online Reinforcement Learning (RL): The framework allows for continuous refinement of the modules through online RL, ensuring that the agents adapt and improve over time.
Performance Metrics and Results
The effectiveness of the proposed framework has been demonstrated across several benchmark tasks, including ALFWorld, WebShop, and SciWorld. In rigorous testing, agents powered by the Qwen3-8B model achieved impressive success rates. Notably, the SFT combined with RL reached:
- ALFWorld: 89.6% success rate on unseen tasks.
- WebShop: 76.8% success rate.
- SciWorld: 66.4% success rate on unseen tasks.
These results not only match or surpass previous published outcomes for agent training but also significantly reduce prompt tokens per turn by a factor of 2 to 7 times compared to traditional ReAct prompting baselines. This demonstrates that procedural context can effectively be transitioned from prompts into model weights, enhancing both efficiency and privacy.
Conclusion
The findings from the study underscore the potential of constant-context skill learning as a transformative approach for deploying LLM agents in personal assistant roles. By addressing the privacy-cost-capability tension, this innovative framework paves the way for more reliable and secure AI applications. As the field continues to evolve, the implications of this research could lead to more sophisticated, efficient, and privacy-conscious AI systems in the future.
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