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Posted: Thu Dec 26, 2024 4:40 am
% of the problems in the MATH dataset, while outcome supervision can only solve 66. Process supervision provides more precise feedback, pointing to the specific location where errors occur, helping the model better assign credit and learn. ) Large reward models can effectively approximate human supervision The study found that large reward models can effectively approximate human supervision for training small reward models, thereby reducing the cost of data collection. This opens up the possibility of large-scale ablation data collection experiments and can be used to evaluate the effectiveness of different supervision methods. ) Activation learning improves the efficiency of process supervision data The study found that activation learning can improve the efficiency of process supervision data by .
6 times, meaning that less data can be brazil email list used to achieve better model performance. Activation learning improves the efficiency of data collection by selecting the most valuable model outputs for manual labeling. ) PRM8K dataset published The PRM8K dataset, which contains 8. labeled step-level data for training a reward model, has been published in the paper. . Stanford & Google : Powering Reasoning with Reasoning ) Main Principles The basic idea of is to use the existing inference capabilities of LLM, the ability of the iterative Bootstrap model to generate reasonable reasoning processes (Rationales) and integrate the reasoning into the training process so that the model can learn to reason.
The basic process is as follows: Inference: The initial dataset has only [Question, Answer First, use several examples with reasoning processes to encourage the model to generate appropriate reasoning processes and answers to the questions in the dataset. Filtering: If the generated answer is correct, the reasoning process is added to the original dataset; if the generated answer is incorrect, try to generate the reasoning process again under the assumption of giving the correct answer. Collect the reasoning that ultimately generates the correct answer and create a fine-tuning dataset [question, reasoning, answer] for fine-tuning. Iteration:Repeat this process, and each time a new dataset is obtained, fine-tuning is initiated from the original model to prevent over-fitting.
6 times, meaning that less data can be brazil email list used to achieve better model performance. Activation learning improves the efficiency of data collection by selecting the most valuable model outputs for manual labeling. ) PRM8K dataset published The PRM8K dataset, which contains 8. labeled step-level data for training a reward model, has been published in the paper. . Stanford & Google : Powering Reasoning with Reasoning ) Main Principles The basic idea of is to use the existing inference capabilities of LLM, the ability of the iterative Bootstrap model to generate reasonable reasoning processes (Rationales) and integrate the reasoning into the training process so that the model can learn to reason.
The basic process is as follows: Inference: The initial dataset has only [Question, Answer First, use several examples with reasoning processes to encourage the model to generate appropriate reasoning processes and answers to the questions in the dataset. Filtering: If the generated answer is correct, the reasoning process is added to the original dataset; if the generated answer is incorrect, try to generate the reasoning process again under the assumption of giving the correct answer. Collect the reasoning that ultimately generates the correct answer and create a fine-tuning dataset [question, reasoning, answer] for fine-tuning. Iteration:Repeat this process, and each time a new dataset is obtained, fine-tuning is initiated from the original model to prevent over-fitting.