This way we highlight the gain obtained thanks to the training of the backbones.

We now specify how we build the DRL behavior dataset and process into a symbolic regression friendly format. It is an indicator of the population of genetic programs' performances. The fitness metric driving our evolution is simply the MSE between the predicted action and the "expert" action (teacher model's action). We specifically follow 5 different evolution schemes, either one picked stochastically. This mutant variant carries forth genetic material from both its sources.

To address this need, we contribute Holistic Evaluation of Multimodal Models ( HEMM), visualized in Figure 1. HEMM, as an evaluation framework, goes beyond conventional lists of datasets to emphasize holistic benchmarking at three levels.

We generate clips from action segments in the EPICK dataset.

Another limitation is that the linear model seems to outperform the rank-one quadratic model; we do not fully understand this effect, as discussed in the last paragraph of section 4. A third limitation is that models need to be averaged across time to obtain a single, deployable model: see Figure 5. A final limitation is that we do not yet have convergence theorems or regret bounds for the passive-aggressive updates in these models; see the second paragraph of section 5. (c) Did you discuss any potential negative societal impacts of your work?

We now specify how we build the DRL behavior dataset and process into a symbolic regression friendly format. It is an indicator of the population of genetic programs' performances. The fitness metric driving our evolution is simply the MSE between the predicted action and the "expert" action (teacher model's action). We specifically follow 5 different evolution schemes, either one picked stochastically. This mutant variant carries forth genetic material from both its sources.

Recent advances in multimodal foundation models have achieved state-of-the-art performance across a range of tasks. These breakthroughs are largely driven by new pre-training paradigms that leverage large-scale, unlabeled multimodal data, followed by instruction fine-tuning on curated labeled datasets and high-quality prompts. While there is growing interest in scaling instruction fine-tuning to ever-larger datasets in both quantity and scale, our findings reveal that simply increasing the number of instruction-tuning tasks does not consistently yield better performance. Instead, we observe that grouping tasks by the common interactions across modalities, such as discovering redundant shared information, prioritizing modality selection with unique information, or requiring synergistic fusion to discover new information from both modalities, encourages the models to learn transferrable skills within a group while suppressing interference from mismatched tasks. To this end, we introduce MINT, a simple yet surprisingly effective task-grouping strategy based on the type of multimodal interaction. We demonstrate that the proposed method greatly outperforms existing task grouping baselines for multimodal instruction tuning, striking an effective balance between generalization and specialization.