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README.md
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---
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license: mit
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datasets:
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- peiyi9979/Math-Shepherd
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language:
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- en
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base_model:
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- deepseek-ai/deepseek-math-7b-base
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pipeline_tag: reinforcement-learning
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---
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## Introduction
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<div align="center">
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<img src="
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</div>
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We present a new framework for PRM by framing it as a $Q$-value ranking problem, providing a theoretical basis for reward modeling that captures inter-dependencies among reasoning states.
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---
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license: mit
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datasets:
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- peiyi9979/Math-Shepherd
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language:
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- en
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base_model:
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- deepseek-ai/deepseek-math-7b-base
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pipeline_tag: reinforcement-learning
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---
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## Introduction
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<div align="center">
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<img src="PQM.png" width="822px">
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</div>
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We present a new framework for PRM by framing it as a $Q$-value ranking problem, providing a theoretical basis for reward modeling that captures inter-dependencies among reasoning states.
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