AI RESEARCH
Verifiable Process Rewards for Agentic Reasoning
arXiv CS.AI
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ArXi:2605.10325v1 Announce Type: new Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of large language models (LLMs), but most existing approaches rely on sparse outcome-level feedback. This sparsity creates a credit assignment challenge in long-horizon agentic reasoning: a trajectory may fail despite containing many correct intermediate decisions, or succeed despite containing flawed ones.