Inducing Process Supervision from Outcome-Only Reinforcement Learning

Key points

• Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning.1
• However, training strong PRMs remains costly: human step annotation is difficult to scale, while Monte Carlo estimation is computationally expensive and can drift from the intrinsic correctness of steps.1
• Outcome-only RL can therefore reinforce step-level verification without explicit process supervision.1
• We validate the effectiveness of TIPS across math and agent benchmarks and four backbone families.1

Sources (1)

• [1]Inducing Process Supervision from Outcome-Only Reinforcement Learning
  Hugging Face Daily Papers · Sep 29, 12:00 AM

  To get effective PRMs at low cost, we introduce TIPS (Thinking-Induced Process Supervision), an outcome-only reinforcement learning (RL) framework for training generative PRMs. In TIPS, the model generates a chain-of-thought (CoT) followed by step-level labels and an outcome label.

  Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning.

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