Learning Composition, source:overseas
Posted: Thu Dec 26, 2024 4:20 am
Reinforcement learning is not a fixed process. It allows the model to continue to ask questions, provide answers, and make decisions as its abilities continue to improve, allowing the model to continuously actively explore the limits of its current capabilities and continuously expand its boundaries. These two factors work together to form the role of counterfactual reasoning, which can unlock the enormous potential of causal learning and give the model stronger reasoning capabilities. .5 PRM and ORM PRM (Process Reward Model) rewards good thinking steps, not just correct results. This is closer to human learning and reasoning, and is often implemented by using COT to represent the reasoning process and scoring for each step.
This is possible thanks to the semantic understanding belgium email list capabilities of LLM. In traditional RL, we score according to the final result, and the scoring model is called ORM (Outcome Reward Model), and by specifically training the LLM to become a process verifier, a new scoring model is called PRM, which is often obtained by; fine-tuning a smaller LLM. OpenAI's step-by-step verification is also one of the most important works in recent times. The PRM they trained outperformed the ORM in solving 78.% of the problems in the MATH test dataset. A Google Research paper this year mentioned that when the PRM successfully finds the first error in the process, the effect of RL training can be significantly improved.
.6 Critical Model As the complexity of the task increases,Relying solely on the model’s own reasoning abilities may not provide an effective reward signal. This makes monitoring the complex reasoning process within the model a scalable supervision problem. In particular, the Critic method should also be introduced into the training process of the implicit thought chain of o. By decomposing the reasoning process and using additional stronger and more specialized critic models, the supervision of the reasoning process can be extended to more complex problems. This also alleviates to some extent the sparse problem of determining the reward signal based only on whether the reasoning process can produce the correct result.
This is possible thanks to the semantic understanding belgium email list capabilities of LLM. In traditional RL, we score according to the final result, and the scoring model is called ORM (Outcome Reward Model), and by specifically training the LLM to become a process verifier, a new scoring model is called PRM, which is often obtained by; fine-tuning a smaller LLM. OpenAI's step-by-step verification is also one of the most important works in recent times. The PRM they trained outperformed the ORM in solving 78.% of the problems in the MATH test dataset. A Google Research paper this year mentioned that when the PRM successfully finds the first error in the process, the effect of RL training can be significantly improved.
.6 Critical Model As the complexity of the task increases,Relying solely on the model’s own reasoning abilities may not provide an effective reward signal. This makes monitoring the complex reasoning process within the model a scalable supervision problem. In particular, the Critic method should also be introduced into the training process of the implicit thought chain of o. By decomposing the reasoning process and using additional stronger and more specialized critic models, the supervision of the reasoning process can be extended to more complex problems. This also alleviates to some extent the sparse problem of determining the reward signal based only on whether the reasoning process can produce the correct result.