RL fine-tuning of LLMs improves clean-benchmark accuracy while degrading accuracy under three injected-distractor evaluation scenarios, though one of the three scenarios contradicts the headline claim.
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Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning
RL fine-tuning of LLMs improves clean-benchmark accuracy while degrading accuracy under three injected-distractor evaluation scenarios, though one of the three scenarios contradicts the headline claim.