arXiv:2607.05184v1 Announce Type: new Abstract: Self-distillation is a promising recipe for self-improvement in language models. In this setting, a model can serve as its own teacher when given privileged information, such as a solution to a math problem. This seems especially appealing for thinking models, which can use test-time reasoning to absorb the privileged information. Surprisingly, we show that privileged self-distillation degrades thinking models on long reasoning traces: across five Qwen3 and OLMo thinking models evaluated on AIME24, AIME25, and HMMT25, privileged-context distillat
This paper offers timely findings as large language models (LLMs) are increasingly relied upon for complex reasoning tasks and self-improvement mechanisms are a key area of development.
Sustained progress in AI hinges on effective self-improvement, and understanding the limitations of current techniques like privileged self-distillation is crucial for future research and development.
The effectiveness of a previously promising self-distillation technique, particularly for advanced 'thinking models' engaged in long reasoning chains, is now called into question, necessitating a re-evaluation of self-improvement strategies.
- · AI researchers focusing on alternative self-improvement methods
- · Developers prioritizing robust, non-distillation based reasoning enhancements
- · LLM developers heavily relying on privileged self-distillation for thinking mode
- · Existing self-distillation methodologies
This research will likely redirect significant effort away from privileged self-distillation for complex reasoning tasks in LLMs.
New techniques for sustained, beneficial self-improvement in LLMs, especially for long reasoning, will become a more pressing research priority.
The development trajectory of advanced AI could be altered if fundamental self-improvement mechanisms prove more challenging to implement effectively than previously assumed.
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Read at arXiv cs.AI