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Experimental results demonstrate the proposed ${M}^2$Tok, a Multi-head Multi-codebook Action Tokenizer designed to minimize reconstruction error and enhance policy performance, achieves superior reconstruction fidelity and significantly boosts the success rate of VLA models.
This work introduces MINT-Safe, an open-source visual multi-turn training dataset comprising 11,270 multi-image dialogues and 500 refusal VQA pairs, and proposes TAD-Align, a dialogue safety alignment framework centered on a turn-aware dual-objective reward function.
Omni-RewardBench is introduced, the first benchmark for comprehensive evaluation of ORMs across modalities and demonstrates that current OLLMs fall short as reward models, revealing several common failure modes such as perception failure, modality dominance failure, and cross-modal fusion failure.
FISA is proposed, a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases that generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion.
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