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6 件Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, an
We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training
Entropy control has become an effective tool in reinforcement learning (RL) of large language models (LLMs), h
Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post
Reinforcement learning with verifiable rewards (RLVR) commonly uses entropy for advantage shaping. However, en
Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However