Скачать или смотреть SHAPE OF THOUGHT: WHEN DISTRIBUTION MATTERS MORE THAN CORRECTNESS IN REASONING TASKS

What if training AI on wrong answers actually made it smarter?
In this video, we unpack a surprising research paper titled
“Shape of Thought: When Distribution Matters More Than Correctness in Reasoning Tasks”
by Abhranil Chandra and Ayush Agrawal.
For years, we’ve assumed that better reasoning in large language models comes from perfectly correct, human-written chain-of-thought examples. This paper challenges that belief.
The authors show that language models can improve their reasoning even when trained on synthetic chains of thought that end in incorrect final answers. Why? Because those traces are closer to the model’s natural way of thinking—and often contain partially correct reasoning steps that are still valuable.
We explore:
• Why distribution of training data can matter more than correctness
• How models learn useful reasoning patterns from “wrong” solutions
• Why final-answer accuracy is a poor proxy for reasoning quality
• How paraphrasing human-written solutions can boost performance
• What kinds of errors models can tolerate… and where things break down
If you’re working on LLM reasoning, fine-tuning, or data curation, this paper forces a rethink of what “high-quality” training data really means.
Sometimes, learning from mistakes isn’t a bug…
it’s the feature.
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