Rishabh Agrawal

Training a small advisor to steer frozen frontier LLMs actually degrades if it learns from every post-hoc correction—selectively distilling only the advice that causally shifts execution yields up to a 6.4% gain and robust cross-family model transfer.

Forward cross-entropy not only guarantees monotonic policy improvement but also significantly boosts performance in complex reasoning tasks.