Process
Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework for Enhancing Reasoning Capabilities
Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it challenging to train models that need multi-step reasoning, like those employed in mathematical problem-solving and programming. Additionally, credit assignment becomes ambiguous, as the model does not get…
This AI Paper Explores Reinforced Learning and Process Reward Models: Advancing LLM Reasoning with Scalable Data and Test-Time Scaling
Scaling the size of large language models (LLMs) and their training data have now opened up emergent capabilities that allow these models to perform highly structured reasoning, logical deductions, and abstract thought. These are not incremental improvements over previous tools but mark the journey toward reaching Artificial general intelligence (AGI). Training LLMs to reason well…