arXiv:2605.13217v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge. Agents often receive sparse, trajectory-level rewards only at the end of an episode, making it difficult to determine which intermediate actions contributed to success or failure. As a result, propagating delayed outcomes back to individual decision steps without relying on costly auxiliary value models remains an open problem. We propose Generalized Advantage Grouped Policy O
The rapid advancement of large language models necessitates more effective ways to train complex AI agents for real-world applications.
Improving credit assignment in multi-turn environments is crucial for developing more capable and autonomous AI agents, moving beyond simple task execution.
This research introduces a method to propagate delayed outcomes to individual decision steps without relying on costly auxiliary value models, potentially simplifying and accelerating agent training.
- · AI Research Labs
- · Developers of LLM Agents
- · Industries using autonomous AI agents
- · Developers reliant on auxiliary value models for credit assignment
- · Systems with high computational costs for agent training
More efficient and sophisticated training of AI agents becomes possible, leading to improved performance in complex, multi-step tasks.
The proliferation of more capable AI agents could accelerate automation in various white-collar and specialized workflows.
As agents become more autonomous and reliable, they may begin to independently generate and execute multi-modal plans, expanding their utility and impact across sectors.
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Read at arXiv cs.AI