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Theoretical Modeling of LLM Self-Improvement Training Dynamics Through Solver-Verifier Gap

Created by
  • Haebom

Author

Yifan Sun, Yushan Liang, Zhen Zhang, Jiaye Teng

Outline

This paper theoretically models how the performance of large-scale language models (LLMs) evolves during self-improvement, a technique that improves the performance of LLMs through self-improvement without relying on external data. Specifically, we model the dynamics of self-improvement training using the concept of the solver-verifier gap (the gap between the LLM's solving and verification capabilities) and propose a method for modeling the entire training trajectory based on this dynamic. Experimental results demonstrate the effectiveness of our theoretical framework and analyze the impact of external data on these dynamics. We find that, in environments with limited external data, using external data at any point in time does not significantly impact final performance.

Takeaways, Limitations

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Takeaways:
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Presenting a new theoretical framework for the training dynamics of LLM self-improvement courses.
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Explain the performance improvement of self-improvement by utilizing the solver-verifier gap concept.
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Quantifying the performance limits of self-improvement through theoretical models.
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Analysis of the impact of external data on self-improvement.
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Verify flexibility in using limited external data.
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Limitations:
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Lack of details on specific modeling methodology and experimental results (summary basis).
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Other factors that may have contributed to the performance gains of self-improvement may not have been considered.
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Lack of information on the diversity of LLMs and datasets used in the experiments.
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Lack of in-depth analysis of optimal utilization strategies for external data.
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