Unverified50% confidenceSolutionExact time
The core idea of SPLIT-RL is to separate the training of visual reasoning and language reasoning into two non-overlapping stages.
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50%
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Long-term
Relevance
10/9/2026
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Related Claims
UnverifiedThe policy trained by SPLIT-RL is used during inference in the same way as an ordinary GRPO model, requiring only a single CoT call and adding no inference cost.72% similarVerifiedEach RL training step requires running inference (generating rollouts) and backpropagation (updating weights) simultaneously, making the compute requirements far higher than ordinary fine-tuning.71% similarUnverifiedModels such as o1 and DeepSeek-R1 internalize chain-of-thought as a training objective, generating a large amount of internal draft reasoning before producing the final answer.70% similarUnverifiedModern reasoning models generally use RLHF or its variant RLAIF for alignment training, where the model learns during training when to stop reasoning and when to output an answer.70% similarUnverifiedThe researchers propose a simple, training-free framework that enables reasoning-capable vision-language models to perform iterative search, dynamic reasoning, and evidence accumulation.69% similar
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