GFlowRL: A Scalable Distribution-Matching RL Algorithm for Large Language Models
Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse reasoning paths by matching reward…
Read the full articleYou might also wanna read
Rethinking Reinforcement Learning for Language Models: The SAO Approach
Single-rollout Asynchronous Optimization (SAO) offers a new path for more stable and effective reinforcement learning in large language mode
Study Finds Single Transformer Layer Can Match Full-Parameter RL Training in LLMs
Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how
arxiv.org·14d agoStudy Finds Single Transformer Layer Can Match Full-Parameter RL Training in LLMs
Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how
arxiv.org·14d ago
Supervised Fine-Tuning as Reinforcement Learning: Introducing Importance-Weighted SFT
Behavior Cloning (BC) on curated (or filtered) data is the predominant paradigm for supervised fine-tuning (SFT) of large language models; a
arxiv.org·11mo ago
Fast-dLLM: Training-Free Acceleration Method for Diffusion Language Models Using KV Cache and Parallel Decoding
Diffusion-based large language models (Diffusion LLMs) have shown promise for non-autoregressive text generation with parallel decoding capa
arxiv.org·8mo ago
When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning
arXiv:2607.07976v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capab
New Framework Formalizes Learning from Language Feedback with Provable Performance Guarantees
Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language mode
arxiv.org·1mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.