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cs.LG2024★ 3 cited
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning
Amrith Setlur, Chirag Nagpal, Adam Fisch +6
A promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, po…
cs.LG2024★ 1 cited
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
Amrith Setlur, Saurabh Garg, Xinyang Geng +3
Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question f…