Energy-Based Transformers are Scalable Learners and Thinkers

Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs)-a new class of Energy-Based Models (EBMs)-to assign an energy (unnormalized probability) value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. This formulation enables System 2 Thinking to emerge from unsupervised learning, making it modality and problem agnostic. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Trans-former++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs, and depth. During inference, EBTs improve performance with System 2 Thinking (i.e., extra computation) by 29% more than the Transformer++ on language tasks, and EBTs outperform Diffusion Transformers on image denoising while using fewer forward passes. Further, we find that System 2 Thinking with EBTs yields larger performance improvements on data that is farther out-of-distribution, and that EBTs achieve better results than existing models on most downstream tasks given the same or worse pretraining performance, suggesting that EBTs generalize better than existing approaches. Consequently, EBTs are a promising new paradigm for scaling both the learning and thinking capabilities of models. Introduction In psychology, human thinking is often classified into two different types: System 1 (thinking fast) and System 2 (thinking slow) [1] [2] [3] [4] . System 1 thinking is characterized by quick, intuitive and automatic responses, relying on previous experience to solve simple or familiar problems. Alternatively, System 2 Thinking is slow, deliberate and analytical, requiring conscious effort and logical reasoning to process more complex information. System 2 Thinking is essential for complex problems that go