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Can off-policy guidance really teach models to reason under uncertainty?
Learning to Reason under Off-Policy Guidance
Published: 12 May 2026 · Updated: 13 July 2026
Read the original sourceWhat the paper says
The authors explore using off-policy data (generated by another model) to guide RL training. They claim that leveraging diverse off-policy experience improves reasoning robustness and sample efficiency.
The Critique
Off-policy data may include flawed or unsafe reasoning. The study does not carefully filter the off-policy trajectories or analyse the risk of degeneracy. There is no evaluation on tasks requiring high-stake decision making.
Why It Matters
Off-policy learning could accelerate progress by reusing existing data rather than requiring expensive human feedback.
What They Missed
The authors do not compare with on-policy RL or supervised fine-tuning, making it difficult to isolate the benefits of off-policy guidance.
The Big Question
How can we ensure that off-policy guidance steers models toward better reasoning rather than just reinforcing the status quo?