Contextual Experience Replay for Self-Improvement of Language Agents (2506.06698v1)

Abstract: LLM agents have been applied to sequential decision-making tasks such as web navigation, but without any environment-specific experiences, they often fail in these complex tasks. Moreover, current LLM agents are not designed to continually learn from past experiences during inference time, which could be crucial for them to gain these environment-specific experiences. To address this, we propose Contextual Experience Replay (CER), a training-free framework to enable efficient self-improvement for language agents in their context window. Specifically, CER accumulates and synthesizes past experiences into a dynamic memory buffer. These experiences encompass environment dynamics and common decision-making patterns, allowing the agents to retrieve and augment themselves with relevant knowledge in new tasks, enhancing their adaptability in complex environments. We evaluate CER on the challenging WebArena and VisualWebArena benchmarks. On VisualWebArena, CER achieves a competitive performance of 31.9%. On WebArena, CER also gets a competitive average success rate of 36.7%, relatively improving the success rate of the GPT-4o agent baseline by 51.0%. We also conduct a comprehensive analysis on it to prove its efficiency, validity and understand it better.

Contextual Experience Replay for Self-Improvement of Language Agents

In the field of artificial intelligence and LLM agents, the paper "Contextual Experience Replay for Self-Improvement of Language Agents" introduces a novel framework designed to bolster the performance of LLM agents in complex environments. This framework, named Contextual Experience Replay (CER), is predicated on the insight that LLM agents often falter in intricate sequential decision-making tasks—such as web navigation—due to inadequate environment-specific experiences and a lack of dynamic learning capabilities during inference.

Key Concepts and Methodology

CER differs from conventional LLM approaches by eschewing training processes, instead opting for a mechanism that leverages previous task experiences to improve agent decision-making in real-time. The core of CER lies in its dynamic memory buffer, where distilled past experiences, encapsulating environment dynamics and decision-making patterns, are stored and synthesized. This allows the model to retrieve and apply pertinent experiences when faced with new tasks, enhancing adaptability and performance.

The paper delineates CER's operational structure into several modules:

1. Experience Distillation: This module extracts meaningful dynamics and skills from past trajectories. Dynamics refer to key environmental interactions, while skills relate to decision-making patterns, which are pivotal for navigating complexities in web environments.
2. Experience Retrieval: The retrieval module identifies and processes the most useful experiences for the current task from the memory buffer, ensuring the agent is equipped with relevant knowledge.
3. Contextual Augmentation: Retrieved experiences are integrated into the agent's context window, thereby influencing its decision-making policies and leading to improved actions.

This methodology is tested across various settings, including offline analysis (pre-collected trajectories), online scenarios (learning from self-generated data), and hybrid approaches (combining both offline and online data sources).

Results and Performance

CER's efficacy is evaluated on two realistic benchmarks: WebArena and VisualWebArena. On the WebArena benchmark, CER demonstrates a relative success rate improvement of 51.0% over the GPT-4o baseline, achieving an average success rate of 36.7%. In VisualWebArena, CER surpasses prior methods, such as tree search-based techniques, with fewer token costs, achieving a success rate of 31.9%.

This performance underlines CER's robustness and its potential to significantly enhance language agent efficacy through dynamic, past-experience-based learning. The distilled experiences notably elevate cross-template success rates, signifying generalization capabilities beyond mere memorization of entire tasks.

Practical and Theoretical Implications

The practical implications of CER are manifold:

• Improved Autonomous Capability: With CER, language agents become more adept at handling real-world applications without constant human intervention, highlighting its utility in automating routine tasks efficiently.
• Cost Efficiency: The reduction in token costs, particularly in scenarios like VisualWebArena, suggests a substantial decrease in computational expense, making CER a viable choice for sustained deployment in environments necessitating scalability.
• Adaptability and Long-term Learning: By continuously assimilating experiences, CER ensures agents retain long-term adaptability and learning efficiency, crucial for evolving challenges in varied contexts.

Future Directions

Potential future developments with CER could explore its application beyond web navigation, probing into areas such as robotics and real-world navigation tasks. Moreover, investigating more fine-grained utilization of low-quality, random trajectory data could further augment its robustness and applicability across diverse domains. Strategies to efficiently generate structured trajectory data could also enhance CER’s initial learning capabilities for new environments.

Overall, Contextual Experience Replay presents a compelling advancement in the field of AI, offering a mechanism for continuous self-improvement and environment-specific learning, without the overhead of extensive pre-training, demonstrating its promise for the next generation of autonomous language agents.