Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era

arXiv:2605.18903v1 Announce Type: new Abstract: Vision-Language Models in Continual Learning (VLM-CL) aim to continuously adapt to new multimodal tasks while retaining prior knowledge. The emerging paradigm that couples Multimodal Large Language Models (MLLMs) with Reinforcement Learning with Verifiable Rewards (RLVR) calls for a new pattern to guide continual adaptation. Advances in reasoning capability now make it feasible to impose constraints at the reasoning level. We formalize portability, a sample-level measure of how reusable the previous policy's behavior is on a new task, and empiric

Source: arXiv cs.LG — read the full report at the original publisher.

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