5 papers · 1 filter
Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently
Stanley Wei, Juno Kim
Recent advances in large language models (LLMs) have demonstrated that reinforcement fine-tuning of pretrained base models can lead to significant gains in reasoning performance at…
When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Shuning Shang, Hubert Strauss, Stanley Wei +2
Training language models via reinforcement learning often relies on imperfect proxy rewards, since ground truth rewards that precisely define the intended behavior are rarely avail…
Improved high-dimensional estimation with Langevin dynamics and stochastic weight averaging
Stanley Wei, Alex Damian, Jason D. Lee
Significant recent work has studied the ability of gradient descent to recover a hidden planted direction in different high-dimensional settings, including t…
What Makes a Reward Model a Good Teacher? An Optimization Perspective
Noam Razin, Zixuan Wang, Hubert Strauss +3
The success of Reinforcement Learning from Human Feedback (RLHF) critically depends on the quality of the reward model. However, while this quality is primarily evaluated through a…
Provable unlearning in topic modeling and downstream tasks
Stanley Wei, Sadhika Malladi, Sanjeev Arora +1
Machine unlearning algorithms are increasingly important as legal concerns arise around the provenance of training data, but verifying the success of unlearning is often difficult.…