382 citations · 978 across the 50 of their papers we have counts for
Showing 2025Show all
2 papers · 1 filter
cs.LG2025
To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
Yuda Song, Dhruv Rohatgi, Aarti Singh +1
Partial observability is a notorious challenge in reinforcement learning (RL), due to the need to learn complex, history-dependent policies. Recent empirical successes have used pr…
cs.LG2025
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
Gokul Swamy, Sanjiban Choudhury, Wen Sun +2
From a first-principles perspective, it may seem odd that the strongest results in foundation model fine-tuning (FT) are achieved via a relatively complex, two-stage training proce…