arXiv Machine Learning
3d ago

TraceForge: Distilling with Your Own Thinking

TraceForge introduces a two‑stage training method that generates reasoning traces in the style of a target model family, using supervised learning followed by adversarial refinement. The system produces traces that are judged as authentic with scores comparable to real traces, and when applied to teacher answers, it improves performance on coding benchmarks such as HumanEval+ and MBPP+. The approach addresses the limitation of existing models that only return answers without detailed reasoning, enabling more transparent and learnable outputs.

By Yan Wang
arXiv AI
Oct 9

The Distillation Game: Adaptive Evaluations & Efficient Defenses

The paper introduces a minimax framework to analyze the trade-off between model utility and vulnerability to distillation attacks, featuring an adaptive evaluation rule that reweights high-value examples and a teacher-side defense that suppresses outputs most useful for imitation. It proposes a cheap, forward-pass-only defense called Product-of-Experts (PoE) that combines the teacher with a proxy student during generation. Empirical results show a significant passive–adaptive gap, with adaptive students recovering more capability than passive evaluation suggests, narrowing the robustness gap between expensive defenses and PoE while keeping PoE cheaper and preserving higher-quality reasoning traces.

By Youssef Allouah, Mahdi Haghifam, Sanmi Koyejo, Reza Shokri