8 citations · 19 across the 9 of their papers we have counts for
5 papers · 1 filter
Tail-Likelihood Reinforcement Learning
Shrinivas Ramasubramanian, Daman Arora, Fahim Tajwar +11
Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an important distinction: two policies can achieve the same mean reward whi…
Maximum Likelihood Reinforcement Learning
Fahim Tajwar, Guanning Zeng, Yueer Zhou +7
Reinforcement learning (RL) is the method of choice for training models in setups where the objective function can only be evaluated by sampling from the model. Our key observation…
Shrinking the Variance: Shrinkage Baselines for Reinforcement Learning with Verifiable Rewards
Guanning Zeng, Zhaoyi Zhou, Daman Arora +1
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for post-training large reasoning models (LRMs) using policy-gradient methods such as GRPO.…
SPEED-RL: Faster Training of Reasoning Models via Online Curriculum Learning
Ruiqi Zhang, Daman Arora, Song Mei +1
Training large language models with reinforcement learning (RL) against verifiable rewards significantly enhances their reasoning abilities, yet remains computationally expensive d…
Training Language Models to Reason Efficiently
Daman Arora, Andrea Zanette
Scaling model size and training data has led to great advances in the performance of Large Language Models (LLMs). However, the diminishing returns of this approach necessitate alt…