Inducing Process Supervision from Outcome-Only Reinforcement Learning
TIPS trains generative process reward models with outcome-only RL, reaching 85.2 F1 on ProcessBench.
Process reward models usually need costly human step labels or Monte Carlo estimation. TIPS (Thinking-Induced Process Supervision) trains generative PRMs with outcome-only reinforcement learning: the model emits a chain-of-thought, step labels, and an outcome label, and the reward is whether the outcome matches ground truth. Group-relative advantage then updates the full response, reinforcing accurate step checks when they improve outcome judgments. TIPS-Qwen3-4B-Thinking-2507 scores 85.2 F1 on ProcessBench using 3.2K outcome-labeled trajectories, beating evaluated trained PRMs and prompt judges GPT-5.4-Instruct and Claude-4.7-Opus while trailing o1-mini.
- Outcome-only RL induces step-level verification without explicit process labels.
- Reward uses only whether the predicted outcome matches ground truth.
- TIPS-Qwen3-4B reaches 85.2 ProcessBench F1 with 3.2K trajectories.
- It beats GPT-5.4-Instruct and Claude-4.7-Opus but trails o1-mini.
- Validated on math and agent benchmarks across four backbone families.
- Products
- ProcessBench
From the original article
1 min read at huggingface.co
Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning. 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.
By Shengda Fan, Xin Cong, Zhong Zhang · huggingface.co
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