A team from FAIR at Meta and collaborators from Georgia Tech and StabilityAI have advanced the refinement of large language models (LLMs) with Stepwise Outcome-based and Process-based Reward Models. This innovation significantly improves LLMs’ reasoning accuracy, particularly evident in tests on the LLaMA-2 13B model. The research charts a path for AI systems to autonomously enhance reasoning abilities.
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Advanced Refinement Strategies in AI: Unveiling the Power of Stepwise Outcome-based and Process-based Reward Models
The exploration into refining the reasoning of large language models (LLMs) marks a significant stride in artificial intelligence research, spearheaded by a team from FAIR at Meta alongside collaborators from Georgia Institute of Technology and StabilityAI. These researchers have embarked on an ambitious journey to enhance LLMs’ ability to self-improve their reasoning processes on challenging tasks such as mathematics, science, and coding without relying on external inputs.
Stepwise Outcome-based Reward Models (SORMs): Precision in Refinement
Traditionally, LLMs, despite their sophistication, often need to improve in identifying precisely when and how their reasoning needs refinement. This gap led to the development of Outcome-based Reward Models (ORMs), tools designed to predict the accuracy of a model’s final answer, hinting at when an adjustment is necessary. Yet, a critical observation made by the team was ORMs’ limitations: they were found to be overly cautious, prompting unnecessary refinements even when the model’s reasoning steps were on the right track. This inefficiency prompted a deeper inquiry into more targeted refinement strategies.
Meet Stepwise ORMs (SORMs), the novel proposition by the research team. Unlike their predecessors, SORMs are adept at scrutinizing the correctness of each reasoning step, leveraging synthetic data for training. This precision allows for a more nuanced approach to refinement, distinguishing accurately between valid and erroneous reasoning steps, thereby streamlining the refinement process.
Global and Local Refinement Models: A Dual Approach
The methodology employed by the team involves a dual refinement model: global and local. The global model assesses the question and a preliminary solution to propose a refined answer, while the local model zeroes in on specific errors highlighted by a critique. This bifurcation allows for a more granular approach to correction, addressing both broad and pinpoint inaccuracies in reasoning. Training data for both models is synthetically generated, ensuring a robust foundation for the system’s learning process.
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