Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can’t large language models replicate this holistic understanding? Through a systematic analysis of existing training paradigms in vision-language-action models (VLA), we identify two key challenges: spurious forgetting, where robot training overwrites crucial visual-text alignments, and task interference, where competing control and understanding tasks degrade performance when trained jointly. To overcome these limitations, we propose ChatVLA, a novel framework featuring Phased Alignment Training, which incrementally integrates multimodal data after initial control mastery, and a Mixture-of-Experts architecture to minimize task interference. ChatVLA demonstrates competitive performance on visual question-answering datasets and significantly surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks. Notably, it achieves a six times higher performance on MMMU and scores 47.2% on MMStar with a more parameter-efficient design than ECoT. Furthermore, ChatVLA demonstrates superior performance on 25 real-world robot manipulation tasks compared to existing VLA methods like OpenVLA. Our findings highlight the potential of our unified framework for achieving both robust multimodal understanding and effective robot control.

When aligning large language models (LLMs), their performance across various tasks (such as being helpful, harmless, and honest) is heavily influenced by the composition of the training data. However, it is difficult to determine what mixture of data should be used to produce a model with strong performance across all tasks. Existing approaches rely on large ablation studies, heuristics, or human intuition, though these can be prohibitively expensive and suboptimal. We study this problem in the context of preference optimization via DPO and propose a novel and theoretically justified algorithm, AutoMixAlign (AMA), that adaptively mixes datasets during LLM training to balance performance across multiple tasks. AMA first trains specialist models for each task to determine losses that corresponding to strong task performance. Next, AMA trains a generalist model using a novel minimax optimization that prioritizes tasks for which generalist model losses are furthest from specialist model losses. We introduce two algorithms to optimize this problem: (1) AMA-R adaptively reweights the objective to prioritize tasks, and (2) AMA-S adaptively adjusts how much data is sampled from each task to prioritize tasks. Both algorithms achieve a convergence rate of O(1/√T) in the convex case. AMA-R’s convergence result immediately follows from Sagawa et. al, 2019, and we provide a convergence proof for AMA-S using techniques from online learning such as EXP3 (Auer et. al, 2002). We evaluate AMA on several multitask alignment setups, and observe that AMA outperforms the standard alignment approach which simply optimizes the total loss across all tasks and also outperforms model-merging methods.