SSD Unleashed: How Simple Self-Distillation Turns AI Guesses into Mastery

A deep dive into Simple Self-Distillation (SSD): how large language models can improve by training on their own unverified outputs with zero external supervision. We unpack the Precision Exploration Conflict, the roles of locks (need for precision) and forks (creative exploration), and how SSD reshapes token distributions to sharpen precision while preserving exploration. We review the Quinn 330B Instruct results on LiveCodeBench (notable ~30% relative gains and stronger improvements on hard problems) and discuss the surprising finding that even data with gibberish can help models learn the geometry of problem-solving. Finally, we consider what latent capabilities might be unlocked when models learn from their own guesses and what this could mean for AI-assisted problem solving.

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