D‑Skill is a framework that distills the behavioral advantage of a large language‑model teacher into an interpretable natural‑language skill document, using only paired rollouts and no weight updates. The skill is constructed offline from fixed trajectories, then provided to a smaller student model at inference time. Across benchmarks such as SpreadsheetBench‑Verified, ALFWorld, and chess puzzles, D‑Skill closes 75‑95% of the teacher‑student performance gap and reduces inference cost by 5.7×–8.3× compared to the teacher.
By Shuo Yuan, Dacheng Li, Joseph E. Gonzalez
Selective Residual Adaptation (SRA) is a plug‑in framework that improves multi‑step reasoning in distilled large language models by correcting sparse distribution divergences between teacher and student along reasoning paths. It uses a Discrepancy Gate to spot divergence points and a Candidate Residual Adapter to calibrate top‑K logits, thereby avoiding changes to the student backbone or runtime teacher computation. Experiments on Qwen3 models from 0.6B to 4B show that SRA raises average Pass@16 by up to 4.37 percentage points while maintaining general capabilities.
By Zhenxing Dou, Kun He, Mingyang Yi, Yong Liu
KDFP is a new white‑box methodology for general knowledge distillation in large language models, addressing a gap left by prior work that focused mainly on post‑training abilities such as instruction following and chain‑of‑thought reasoning. The authors evaluate lessons from earlier studies and conduct new experiments to design a distillation approach tailored to modern LLMs. KDFP achieves 1.6%–4.9% higher performance across nine benchmarks and improves training efficiency by up to 99.1% through reduced temporary parameters.
By Ryan Swift, Konstantinos Psounis
The paper introduces Universal Textual Teaching (UTT), a parameter‑update‑free framework that distills knowledge gaps between a Teacher and Student large language model into a reusable natural‑language artifact called a Primer. UTT identifies representative gap cases, then iteratively refines the Primer through a cycle of student attempts, prompter‑generated teaching instructions, teacher demonstrations, and synthesizer consolidation. Experiments on math (Omni‑MATH‑2) and code generation (KernelBench) show significant accuracy gains, outperforming prompt engineering and parameter‑based knowledge distillation, and the Primer generalizes across different Teacher‑Student pairs.
By Zhanyi Lu, Huan Wang
The paper introduces masked self‑distillation, a post‑training framework that trains a language model to internalize portions of its own intermediate reasoning traces. By varying the fraction of trace internalized, the authors demonstrate that models can achieve higher inference efficiency and improved task performance on math and graph‑coloring problems. Experiments on Qwen3‑4B and Qwen3‑8B show that the method generalizes well to in‑domain out‑of‑distribution cases without catastrophic forgetting, and that supervised fine‑tuning alone can reduce trace length at the expense of generalization.
By Durgesh Kalwar, Vardhan Palod, Jaya Adithya Pavuluri, Subbarao Kambhampati
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