Daily AI Paper

Today’s standout paper is about a simple but surprisingly powerful way to make reasoning models explore more. Reinforcement learning with verifiable rewards has helped language models get much better at math and logic, but it often makes them less diverse: the model becomes overconfident, repeats similar reasoning paths, and loses the ability to recover alternative solutions when the first attempt fails. The authors tackle that entropy collapse by borrowing a partial reasoning prefix from a smaller, weaker model and forcing the stronger model to continue from there. Those unfamiliar prefixes act like a nudge out of its comfort zone, pushing it to try different chains of thought instead of following the same well-worn route. The result is better coverage of possible solutions, especially when you care about pass@k rather than just one answer. Across multiple math benchmarks, this approach improves performance without extra supervised fine-tuning, complicated reward shaping, or heavy prompting.