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Black-Box On-Policy Distillation of Large Language Models research paper by Microsoft, 2025

Microsoft · Nov 13, 2025 · Alignment and safety · 47 citations · 54 upvotes · unverified

Read on arXiv

What it shows

Generative Adversarial Distillation (GAD) enhances black-box distillation by framing the student model as a generator and using a discriminator to provide adaptive feedback, surpassing traditional sequence-level knowledge distillation.

UnverifiedHugging Face's summary; not yet checked by hand.

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Other alignment and safety papers

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Training language models to follow instructions with human feedbackInstructGPT: fine-tuning on human feedback made a 1.3B model preferred over the 175B GPT-3.OpenAI · Mar 202224.3k
Direct Preference Optimization: Your Language Model is Secretly a Reward ModelDPO aligns a model to human preferences with a simple classification loss, no reward model or RL loop.Stanford University · May 202310.6k
Constitutional AI: Harmlessness from AI FeedbackConstitutional AI trains a harmless assistant from AI feedback guided by a short list of written principles, not human harm labels.Anthropic · Dec 20223,721
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingModels trained with a hidden backdoor kept their deceptive behaviour through standard safety training.Anthropic · Jan 2024578
Alignment faking in large language modelsClaude 3 Opus sometimes went along with a training goal it disagreed with, to avoid being changed, without being told to.Anthropic · Dec 2024343
Why Language Models HallucinateModels hallucinate because training and benchmarks reward confident guessing over saying they do not know.OpenAI · Sep 2025331
About this paper
Authors
Tianzhu Ye, Li Dong, Zewen Chi and 3 more
arXiv
2511.10643 · PDF
Venue
arXiv.org
Citations
47, 3 influential · Semantic Scholar
Upvotes
54 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 3Sep 25, 2026
Citationsfirst count: 47Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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