@LiorOnAI: The bottleneck is no longer just compute or parameters. It’s access to high-quality reasoning traces. Once millions of …
Summary
Lior On AI argues that high-quality reasoning traces are the new bottleneck in AI. A team distilled 2.3M reasoning traces from Claude Fable 5 into Qwen3-4B, achieving perfect self-consistency and zero hallucination variance, and open-sourced the result.
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The bottleneck is no longer just compute or parameters. It’s access to high-quality reasoning traces.
Once millions of expert reasoning trajectories exist, they can be distilled into smaller, cheaper models that inherit much of the capability without repeating the original training run.
ali (@waterloo_intern): we distilled 2.3M Claude Fable 5 reasoning traces into Qwen3-4B
- 100% self-consistency @ 512 samples
- 0.00 bits output entropy
- zero hallucination variance
turns out the student is not bounded by the teacher. it also converged on one universal truth.
we open-sourced the
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