13 citations · 24 across the 15 of their papers we have counts for
5 papers · 1 filter
EPIG-Tree: Compute-Optimal Branching for Gradient-Efficient Reinforcement Learning
Nikita Khomich, Leopold Hermansson, Ido Hakimi
Reward-based reinforcement learning for language models, exemplified by Group Relative Policy Optimization (GRPO), collapses an entire stochastic trajectory into a single scalar re…
Majority Voting for Code Generation
Tim Launer, Jonas Hübotter, Marco Bagatella +2
We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies a representative solution f…
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
Davit Melikidze, Marian Schneider, Jessica Lam +4
Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring…
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
Daniel Yang, Samuel Stante, Florian Redhardt +5
Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncert…
Reinforcement Learning via Self-Distillation
Jonas Hübotter, Frederike Lübeck, Lejs Behric +8
Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods for reinforcement learning with ve…