6 papers
Random Remasking Scales Masked Diffusion Inference
Zhaoyi Zhou, Frank Wu, Andrea Zanette
Masked diffusion models (MDMs) generate sequences by progressively unmasking tokens, but errors introduced during decoding are typically irreversible. Recent work has shown that it…
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…
Accelerating Unbiased LLM Evaluation via Synthetic Feedback
Zhaoyi Zhou, Yuda Song, Andrea Zanette
When developing new large language models (LLMs), a key step is evaluating their final performance, often by computing the win-rate against a reference model based on external feed…
Fast Best-of-N Decoding via Speculative Rejection
Hanshi Sun, Momin Haider, Ruiqi Zhang +6
The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human p…
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement
Ruiqi Zhang, Yuexiang Zhai, Andrea Zanette
What can an agent learn in a stochastic Multi-Armed Bandit (MAB) problem from a dataset that contains just a single sample for each arm? Surprisingly, in this work, we demonstrate…