3 papers
cs.CL2026
How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data
Joel Niklaus, Atsuki Yamaguchi, Michal Štefánik +9
Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and s…
cs.AI2026
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems
LM-Provers, Yuxiao Qu, Amrith Setlur +6
Proprietary AI systems have recently demonstrated impressive capabilities on complex proof-based problems, with gold-level performance reported at the 2025 International Mathematic…
cs.LG2025
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur +4
Training models to effectively use test-time compute is crucial for improving the reasoning performance of LLMs. Current methods mostly do so via fine-tuning on search traces or ru…