We have 3 of 19 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
TIPS (Thinking-Induced Process Supervision), an outcome-only reinforcement learning (RL) framework for training generative PRMs, is introduced and validated, demonstrating the effectiveness of TIPS across math and agent benchmarks and four backbone families.
This work finds that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model, and proposes AgentRM, a generalizable reward model, to guide the policy model for effective test-time search.
Analyzing a large corpus of publicly released post-training trajectories, it is found that across different tasks, the agent's training strategy is locked in at the very beginning, and the entire remaining budget is spent on local adjustments within the selected strategy.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
