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Self-play helped AI achieve superhuman performance in Go, so why hasn’t it done the same for LLMs? Researchers have found a solution.

Reddit r/singularity · 2d ago

Researchers introduce Self-Guided Self-Play (SGS), a self-play algorithm for LLMs that prevents reward hacking by using a Guide role to score synthetic problems. Applied to theorem proving in Lean4, SGS surpasses RL baselines and allows a 7B model to outperform a 671B model.

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Formal Conjectures: An Open and Evolving Benchmark for Verified Discovery in Mathematics

arXiv cs.AI · 3d ago Cached

This paper introduces Formal Conjectures, an evolving benchmark of 2615 mathematical statements formalized in Lean 4, including open research conjectures for proof discovery and solved problems for auto-formalization, designed to evaluate automated reasoning systems with zero contamination.

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@leloykun: [WIP] Blog post on Lean4-to-TileLang Tensor Program Superoptimizer here:

X AI KOLs Following · 5d ago Cached

A technical blog post introduces a Lean4-to-TileLang tensor program superoptimizer that automatically generates optimized GPU/TPU kernels and hyperparameter scaling laws, demonstrating performance gains over torch.compile.

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@leloykun: I lost track of time again >.< I'm really sorry if you DMed me lately. I promise to go over my DMs! --- This sprint, I …

X AI KOLs Following · 5d ago

The author developed a Lean4-to-TileLang tensor program superoptimizer that automatically generates optimized accelerator kernels and derives hyperparameter scaling laws, achieving a 1.8x speedup on A100 GPUs.

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Formalizing statistical learning theory in Lean 4 [R]

Reddit r/MachineLearning · 2026-05-08 Cached

FormalSLT is a Lean 4 library that formally proves finite-sample statistical learning theory results (ERM, VC bounds, Rademacher bounds, PAC-Bayes, etc.) with explicit assumptions and zero sorry statements, providing a machine-checked foundation for ML theory.

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