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Found 2 papers, 1 papers with code
AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
Most existing large-scale RL systems for LLMs are synchronous, alternating generation and training in a batch setting where rollouts in each training batch are generated by the same model.
On Designing Effective RL Reward at Training Time for LLM Reasoning
In this work, we evaluate popular reward models for RL training, including the Outcome-supervised Reward Model (ORM) and the Process-supervised Reward Model (PRM), and train a collection of LLMs for math problems using RL by combining these learned rewards with success rewards.
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