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Does RL Incentivize Reasoning in LLMs Beyond the Base Model?
Reasoning LLMs Are Just Efficient Samplers: RL Training Elicits No Transcending Capacity
The results show base models maintain broader reasoning coverage despite RL's initial accuracy gains. The improvements from RLVR in visual reasoning align with those seen in math and coding benchmarks, indicating that the original model already covers a broad range of solvable problems, even in multimodal tasks. The consistency across domains suggests that RLVR enhances reasoning capabilities without fundamentally altering the model's problem-solving approach.
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