arXiv:2607.04963v1 Announce Type: new Abstract: Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed rewards often lead to trajectory neglect, in which agents lose focus on the task goal and interaction history at intermediate steps. Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence and therefore provide unreliable estimates of decision reliability. To address this issue, we propose normali
The rapid advancement and deployment of LLM agents are exposing critical training challenges, necessitating innovative solutions like STAPO to improve their reliability and performance on complex tasks.
Improving the training efficiency and reliability of LLM agents is crucial for their broader adoption and for realizing their potential to automate complex white-collar tasks.
This research introduces a more effective method for training LLM agents, potentially leading to more robust and goal-oriented AI systems that are less prone to 'trajectory neglect'.
- · AI agent developers
- · Companies deploying LLM agents
- · Reinforcement learning researchers
- · Less efficient LLM agent training methodologies
- · Developers reliant on traditional RL for agents
More capable and reliable LLM agents become available for a wider array of applications.
Increased trust and adoption of AI agents across various industries, leading to significant productivity gains.
Accelerated development of fully autonomous AI systems capable of managing complex, long-horizon projects without human intervention.
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Read at arXiv cs.AI