24 citations · 56 across the 15 of their papers we have counts for
3 papers · 1 filter
Adversarial Training for Process Reward Models
Gurusha Juneja, Deepak Nathani, William Yang Wang
Process Reward Models (PRMs) enhance reasoning ability of LLMs by providing step-level supervision. However, their widespread adoption is limited due to expensive manual step-level…
Understanding the Effects of Domain Finetuning on LLMs
Eshaan Tanwar, Deepak Nathani, William Yang Wang +1
Large Language Models (LLMs) fine-tuned for specific domains exhibit strong performance; however, the underlying mechanisms by which this fine-tuning reshapes their parametric spac…
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
Deepak Nathani, Lovish Madaan, Nicholas Roberts +14
We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. This is the first Gym environment for machine…