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ExpRL Applies Dense LLM-Judge Rewards for Stronger LLM Mid-Training

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Tanishq Mathew Abraham, Ph.D.@iScienceLuvr#613inTech

ExpRL: Exploratory RL for LLM Mid-Training

Use RL directly for mid-training. An LLM judge compares the sampled reasoning trace against the reference solution and assigns outcome-level or process-level dense rewards. This lets ExpRL reinforce partial progress, useful intermediate reductions, and productive reasoning behaviors that sparse final-answer rewards often fail to upweight.

On challenging math reasoning tasks, ExpRL yields stronger RL priming than SFT, sparse-reward GRPO, and self-distillation, and provides a better initialization for subsequent sparse-reward RL.

4:39 AM · Jun 16, 2026 · 1.1K Views

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Tanishq Mathew Abraham, Ph.D.@iScienceLuvr

code: https://github.com/violetxi/ExpRL abs: https://arxiv.org/abs/2606.17024

Tanishq Mathew Abraham, Ph.D.@iScienceLuvr

ExpRL: Exploratory RL for LLM Mid-Training

On challenging math reasoning tasks, ExpRL yields stronger RL priming than SFT, sparse-reward GRPO, and self-distillation, and provides a better initialization for subsequent sparse-reward RL.

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