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ExpRL outperforms traditional reinforcement learning methods by effectively rewarding intermediate reasoning steps, leading to better LLM performance on complex tasks.
LLMs, like humans, exhibit a "frequency bias," performing better when prompted and fine-tuned with more common textual expressions.
