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All SourcesarXiv
#diversity collapse

Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening

Intermediate
Xiaotong Ji, Rasul Tutunov et al.Jan 29arXiv

The paper shows a fast, training-free way to boost an LLM’s step-by-step reasoning by smartly reusing the model’s own probabilities.

#power distribution sampling#distribution sharpening#low-temperature sampling

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

Intermediate
Shobhita Sundaram, John Quan et al.Jan 26arXiv

This paper teaches a model to be its own teacher so it can climb out of a learning plateau on very hard math problems.

#meta-reinforcement learning#teacher-student self-play#grounded rewards