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1. How Capable Can a Transformer Become? A Study on Synthetic, Interpretable Tasks

   Ramesh, Rahul; Mikail Khona; Robert P. Dick; Hidenori Tanaka; Ekdeep Singh Lubana. (November 2023, Proc. NeurIPS Wkshp. on Symmetry and Geometry in Neural Representations)

   Transformers trained on huge text corpora exhibit a remarkable set of capabilities. Given the inherent compositional nature of language, one can expect the model to learn to compose these capabilities, potentially yielding a combinatorial explosion of what operations it can perform on an input. Motivated by the above, we aim to assess in this paper “how capable can a transformer become?”. In this work, we train Transformer models on a data-generating process that involves compositions of a set of well-defined monolithic capabilities and show that: (1) Transformers generalize to exponentially or even combinatorially many functions not seen in the training data; (2) composing functions by generating intermediate outputs is more effective at generalizing to unseen compositions; (3) the training data has a significant impact on the model’s ability to compose functions (4) Attention layers in the latter half of the model seem critical to compositionality. 

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2. Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task

   Okawa, Maya; Ekdeep Singh Lubana; Robert P. Dick; Hidenori Tanaka. (December 2023, Proc. Conf. on Neural Information Processing Systems)

   Modern generative models exhibit unprecedented capabilities to generate extremely realistic data. However, given the inherent compositionality of real world, reliable use of these models in practical applications mandates they exhibit the ability to compose their capabilities, generating and reasoning over entirely novel samples never seen in the training distribution. Prior work demonstrates recent vision diffusion models exhibit intriguing compositional generalization abilities, but also fail rather unpredictably. What are the reasons underlying this behavior? Which concepts does the model generally find difficult to compose to form novel data? To address these questions, we perform a controlled study of compositional generalization in conditional diffusion models in a synthetic setting, varying different attributes of the training data and measuring the model's ability to generate samples out-of-distribution. Our results show that: (i) the compositional structure of the data-generating process governs the order in which capabilities and an ability to compose them emerges; (ii) learning individual concepts impacts performance on compositional tasks, multiplicatively explaining sudden emergence; and (iii) learning and composing capabilities is difficult under correlations. We hope our study inspires further grounded research on understanding capabilities and compositionality in generative models from a data-centric perspective. 

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3. Contrastive Point Cloud Pretraining for Enhanced Transformers

   https://doi.org/10.1109/ICTAI62512.2024.00057  

   Koti, Divyashree Shivalingappa; Phillips, Joshua L; Cottle, Frederick S (October 2024, IEEE)

   Transformers, although first designed for sequence processing, can also handle unordered sets like point cloud data. Additionally, contrastive pretraining has emerged as a successful technique in image processing but remains unexplored for point cloud data. We develop and integrate a new point cloud pretraining technique inspired by the Simple Framework for Contrastive Learning (SimCLR) into the Set Transformer (ST) and Point Cloud Transformer (PCT) architectures and explore model performance using a novel 3D body scan dataset and the canonical datasets ShapeNet and ModelNet. For the 3D body scan dataset, this integration boosts initial training performance and maintains overall higher performance for classification tasks, and demonstrates better stability/convergence for regression tasks in comparison to non-pretrained (Naïve] counterparts. Furthermore, experiments examining strong generalization (relative performance on previously unseen classes) show improvement for pretrained models compared to Naïve models. Consistent benefits across tasks and data sets are observed based on additional experiments performed on the ShapeNet core dataset. Overall, we show how contrastive pretraining for point cloud data is a viable strategy for improving the performance of Transformers on downstream tasks and accelerating the training process. 

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4. Chop & Learn: Recognizing and Generating Object-State Compositions

   https://doi.org/10.1109/ICCV51070.2023.01852  

   Saini, Nirat; Wang, Hanyu; Swaminathan, Archana; Jayasundara, Vinoj; He, Bo; Gupta, Kamal; Shrivastava, Abhinav (October 2023, Proceedings of ICCV)

   Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting objects in different styles and the resulting object state changes. We propose a new benchmark suite Chop & Learn, to accommodate the needs of learning objects and different cut styles using multiple viewpoints. We also propose a new task of Compositional Image Generation, which can transfer learned cut styles to different objects, by generating novel object-state images. Moreover, we also use the videos for Compositional Action Recognition, and show valuable uses of this dataset for multiple video tasks. Project website: https://chopnlearn.github.io. 

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5. In-Context Reinforcement Learning From Suboptimal Historical Data

   Dong, Juncheng; Guo, Moyang; Fang, Ethan X; Yang, Zhuoran; Tarokh, Vahid (August 2025, ICML Proceedings)

   Transformer models have achieved remarkable empirical successes, largely due to their in-context learning capabilities. Inspired by this, we explore training an autoregressive transformer for in-context reinforcement learning (ICRL). In this setting, we initially train a transformer on an offline dataset consisting of trajectories collected from various RL tasks, and then fix and use this transformer to create an action policy for new RL tasks. Notably, we consider the setting where the offline dataset contains trajectories sampled from suboptimal behavioral policies. In this case, standard autoregressive training corresponds to imitation learning and results in suboptimal performance. To address this, we propose the Decision Importance Transformer (DIT) framework, which emulates the actor-critic algorithm in an in-context manner. In particular, we first train a transformer-based value function that estimates the advantage functions of the behavior policies that collected the suboptimal trajectories. Then we train a transformer-based policy via a weighted maximum likelihood estimation loss, where the weights are constructed based on the trained value function to steer the suboptimal policies to the optimal ones. We conduct extensive experiments to test the performance of DIT on both bandit and Markov Decision Process problems. Our results show that DIT achieves superior performance, particularly when the offline dataset contains suboptimal historical data. 

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