The Signal

Everything that matters in AI, with our take.

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arXiv cs.AIPaper12d ago

Recursive Harness Self-Improvement for Frontier Reasoning Data Synthesis

This is a concrete technique for building harder synthetic data without model weight changes or external verification. If you're training reasoning models and currently stuck at a capability plateau, this method for recursive task evolution is worth implementing. The 54.8% end accuracy suggests the approach generates genuinely difficult problems.

research training synthetic-data reasoning

arXiv cs.CLPaper22d ago

Self-Play Pretraining with Zero Data

This attacks the data bottleneck that will eventually constrain all frontier models. If two models can bootstrap meaningful structure from random initialization through mutual refinement, you decouple scaling from human annotation budgets. The catch: the excerpt cuts off before results, so the real question is whether the learned structure generalizes or collapses into noise. Watch for benchmarks.

research pretraining synthetic-data