12 citations · 21 across the 18 of their papers we have counts for
4 papers · 1 filter
Progress over Points: Reframing LM Benchmarks Around Scientific Objectives
Alwin Jin, Sean M. Hendryx, Vaskar Nath
Current benchmarks that test LLMs on static, already-solved problems (e.g., math word problems) effectively demonstrated basic capability acquisition. The natural progression has b…
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
Anisha Gunjal, Anthony Wang, Elaine Lau +4
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for complex reasoning tasks with clear correctness signals such as math and coding. However, extending it…
Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models
Vaskar Nath, Elaine Lau, Anisha Gunjal +3
We study the process through which reasoning models trained with reinforcement learning on verifiable rewards (RLVR) can learn to solve new problems. We find that RLVR drives perfo…
Planning In Natural Language Improves LLM Search For Code Generation
Evan Wang, Federico Cassano, Catherine Wu +7
While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize tha…