arXiv:2606. 11173v1 Announce Type: new Abstract: Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response.
By Semih Kara, O\u{g}uzhan Ersoy
The paper reviews On‑Policy Self‑Distillation (OPSD), a method where a language model learns from its own generations using privileged information such as reference solutions or plans, eliminating the need for a larger teacher model. It identifies a key failure mode—collapse, where the model’s reasoning paths narrow progressively—and analyzes it through three levers: signal application, privileged information, and teacher dynamics. The review focuses on mathematical reasoning, offering a unified vocabulary and distinguishing settled facts from ongoing debates.
By Justin Robert, Raheel Qader
arXiv:2609.37041v1 Announce Type: cross
Abstract: Self-Distillation Fine-Tuning (SDFT) enables a language model to act as its own teacher: by conditioning on a demonstration, the model produces an im...
By Su Ee Tan, Xiaotong Ji, Rasul Tutunov, Haitham Bou-Ammar, Matthieu Zimmer
The paper investigates how on-policy self‑distillation can alter a model’s behavior by conditioning on privileged information. It contrasts attractive self‑distillation, which pulls a model toward a privileged teacher, with repulsive self‑distillation, which pushes it away, showing that attraction reduces exploratory reasoning while repulsion lengthens responses and can destabilize the model. The authors propose a contrastive self‑distillation objective that combines attraction to a correct‑solution teacher with repulsion from an incorrect‑solution teacher, finding that this approach improves reasoning performance across various model types while keeping response lengths stable.
By Anton Baumann, Akmal Ashirmatov, Leo Schmidt-Traub, Frederike L\"ubeck, Jonas H\"ubotter, Thomas Kleine Buening, Andreas Krause
The paper investigates how privileged information—such as a teacher’s full solution or reasoning trace—affects on‑policy self‑distillation (OPSD) in language models. Using the AMPLE‑Math benchmark, the authors compare distillation with and without extra teacher views, finding that reference‑free distillation explains most gains for Qwen3‑1.7B, while additional references provide modest benefits, especially for polished solutions. The study also shows that the impact of privileged data depends on the student’s training regime and that altering token‑level supervision can leave student behavior largely unchanged.
By XiuYu Zhang, Wei Chow, Junfeng Fang, Zhenkai Liang, Tat-Seng Chua
On-policy distillation trains a language model on its own generations while a teacher scores them token by token. It combines the dense supervision of imitation learning with the on-policy sampling of...