arXiv:2603.17621v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for training LLM-based agents, yet remains limited by low sample efficiency, stemming not only from sparse outcome feedback but also from the agent's inability to leverage prior experience across episodes. While augmenting agents with historical experience offers a promising remedy, existing approaches suffer from a critical weakness: the experience distilled from history is either stored statically or fail to coevolve with the improving actor, causing a progressive misalignment b
The proliferation of LLM-based agents highlights the urgent need for more efficient training paradigms to overcome current limitations related to sample efficiency and experience utilization.
Improving sample efficiency in RL for LLM-based agents is critical for scaling autonomous AI systems, reducing computational costs, and accelerating development cycles across various applications.
This research proposes a method for agents to continuously leverage and update prior experience, potentially leading to more adaptable and generalizable AI agents that learn faster.
- · AI research labs
- · Companies developing AI agents
- · Cloud computing providers
- · Software developers integrating agents
- · Inefficient RL training approaches
- · Systems highly reliant on human supervision for agent training
More sophisticated and capable LLM-based agents can be deployed with fewer computational resources and less training data.
This efficiency gain could accelerate the development and adoption of autonomous AI in complex industries, leading to new service models and automation levels.
The widespread deployment of highly efficient AI agents may reshape job markets and necessitate new human-AI collaboration paradigms.
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Read at arXiv cs.LG