Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation
arXiv:2606.18844v1 Announce Type: new Abstract: Self-distillation improves reasoning in large language models by using the model's own rollouts as training signal, typically through implicit logit-level alignment that minimizes KL divergence toward a privileged target distribution. However, because this supervision is generated via uncontrolled sampling, it provides no diagnostic insight into the model's specific errors or corrective guidance for its individual failure patterns. Consequently, the model learns to imitate a privileged distribution rather than receiving fine-grained corrections t
The paper addresses a current limitation in large language model self-distillation, which is a rapidly evolving area of generative AI research seeking to improve model performance and reliability.
Improving how large language models learn from their own errors can lead to more robust, accurate, and less biased AI systems, impacting their real-world applicability.
This new approach to self-distillation shifts from passive imitation to active error diagnosis, potentially making LLMs more introspective and capable of targeted self-correction.
- · AI developers
- · LLM-powered applications
- · Organizations relying on AI reasoning
- · LLM architectures reliant on uncontrolled sampling
Large language models will become more efficient at self-improvement, requiring less external human supervision for refinement.
This could accelerate the development of more autonomous AI agents capable of complex tasks with fewer errors.
Increased reliability and corrigibility of AI could broaden its adoption in critical sectors like finance, healthcare, and engineering, where error rates are highly sensitive.
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Read at arXiv cs.LG