arXiv:2601.03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning. While process-level reward models offer a promising direction, existing LLM-based judges often produce noisy and inconsistent signals because they lack fine-grained, task-specific rubrics to distinguish high-level planning from low-level execution. In this work, we introduce SCRIBE (Skill-Conditioned Reward with Intermediate Behavioral Evaluation), a reinforcement learning framework that interven
The increasing complexity of AI models and their multi-step reasoning capabilities necessitates more sophisticated supervision mechanisms to ensure reliability and performance.
Improving the reliability and performance of tool-augmented language models is critical for their practical deployment across diverse applications, advancing the capabilities of AI agents.
The introduction of SCRIBE proposes a more granular, skill-conditioned reward system for training AI agents, moving beyond high-level planning to robustly optimize low-level execution.
- · AI agent developers
- · Reinforcement learning researchers
- · SaaS platforms adopting AI agents
- · Industries seeking automated workflows
- · Developers relying solely on high-level reward models
- · Companies with less sophisticated AI training methodologies
Tool-augmented language models become more reliable and capable of complex, multi-step tasks.
The improved performance of AI agents accelerates the automation of white-collar tasks, impacting various service sectors.
Enhanced AI agent capabilities could lead to new forms of human-AI collaboration and potentially autonomous economic actors.
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Read at arXiv cs.AI