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OpenEBFM: Energy-based Foundation Models Are Scalable Architecture
Yuan Pu, Yudong Lu, Ting Wang, Zhicong Hong, Qinuo Li, Xuansheng Wu, Na Zou, Xia Hu, Guanchu Wang
TL;DR
Energy-based models have largely been evaluated in specialized domains, and their general-purpose foundation-model capabilities remain unestablished. EBFMs integrate energy steering and token generation to refine latent states before generating tokens. They show promising scaling with increased model size and training data and promising zero-shot performance across 26 tasks, while training efficiency and model sharding remain constrained.
Problem
Whether energy-based models, typically designed for single tasks or domains, can achieve general-purpose foundation-model capabilities remains unestablished.
Source: §1 · p. 1Method
EBFMs use an energy head to steer latent states toward lower-energy regions before a generative head uses them to predict tokens.
Source: §1 · p. 2Results
EBFMs show promising scaling with increasing model size and training data and promising zero-shot performance across 26 downstream tasks, with consistent advantages over GPTs.
Source: §1 · p. 2Takeaways & Limitations
The findings identify EBFMs as a promising foundation architecture for general-purpose tasks.
Source: §7 · p. 9Pre-training efficiency limits scaling: the model cannot readily be sharded across GPUs, constraining size to a single H200 GPU's memory capacity.
Source: §6 · p. 9
Abstract
from arXiv · showhide
Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios. To bridge this gap, we design energy-steered models (ESMs) for foundational language modeling. ESMs integrate energy steering and token generation within a single architecture to leverage the energy-guided search to refine token generation. Specifically, ESMs first optimize a latent state over the energy landscape, then leverages the refined latent state for token generation. By performing such energy-guided pre-training on large-scale corpora, ESMs learn generally effective energy landscape where favorable tokens correspond to lower energy values. To explore the scaling behavior of ESMs, we pre-train ESMs on three large-scale corpora, then evaluate across 26 downstream tasks spanning a broad range of domains. Our results demonstrate that ESMs have promising scaling behaviors with increasing model size and training data, and outperform GPTs on most downstream tasks. These findings highlight ESMs as a promising architecture for large-scale pre-training and general purpose problem solving. We fully release the model weights and training infrastructure to enable the community to completely reproduce and extend our results. GitHub: https://github.com/datamllab/openebfm HuggingFace: https://huggingface.co/collections/guan-wang/openebfm
1 Introduction
The introduction asks whether energy-based models, previously concentrated in specialized domains, can support general-purpose foundation modeling. It presents EBFMs as an energy-steered architecture and reports favorable scaling and zero-shot results across diverse tasks.
1 Introduction
- Existing EBMs are typically task- or domain-specific, leaving their generalizability across diverse tasks and domains largely unexplored.Whether EBMs can achieve general-purpose capabilities remains open.Source: §1 · p. 1
- EBFMs combine energy steering with token generation: an energy head steers a latent variable toward lower-energy regions before a generative head predicts tokens.Source: §1 · p. 2
- EBFMs show promising zero-shot performance across 26 downstream tasks and consistent advantages over GPTs.The tasks span broad domains and use validation error and accuracy.Source: §1 · p. 2
- EBFMs exhibit favorable scaling with increasing model size and training data compared with GPTs and MDLMs.Source: §1 · p. 2
- The release includes model weights, a training recipe, and infrastructure for community reproduction and extension.Source: §1 · p. 2
2 Preliminary
The preliminary section defines EBFM's shared backbone and two heads, then frames energy steering as a way to use energy evaluation for token generation without enumerating the token space.
2.1 Notations
- EBFM takes a context sequence and latent variable as inputs; its energy and generative heads share a Transformer backbone.The energy head outputs an energy score, while the generative head outputs tokens from the vocabulary.Source: §2.1 · p. 2
2.2 Energy-based Models
- EBMs assign low energy to desirable solutions and high energy to undesirable ones, but naturally act as critics rather than directly generating outputs.Source: §2.2 · pp. 2–3
- Exhaustive token-space search for the lowest-energy solution is computationally infeasible; EBFMs instead use energy evaluation to refine token generation.The generation process steers a latent state toward lower energy before the generative head uses it to produce the next token.Source: §2.2 · p. 3, Figure 2 · p. 3
3 Energy-based Foundation Models
EBFM couples energy steering with token generation: inference refines a context-conditioned latent before token prediction. Training parallelizes steering across sequence positions and optimizes next-token prediction.
3.1 EBFM Inference
- At inference, EBFM refines a context-conditioned latent through K gradient updates, then uses the refined state to generate subsequent tokens.The model keeps θ fixed and computes gradients only with respect to the latent; deterministic updates were reported to yield more stable large-scale pre-training.Source: §3.1 · p. 3
3.2 EBFM Training
- Training concatenates tokens and latent states, then uses redesigned masks and positional indices to steer all positions in one pass while preserving causal dependencies.Each latent state attends only to its prefix tokens and itself.Source: §3.2 · p. 4, Figure 3 · p. 4
- The generative head predicts subsequent-token probabilities from the input sequence and optimized latent states, and θ is updated to minimize the resulting loss.The training objective is negative log likelihood of the target token sequence.Source: §3.2 · p. 4
3.3 Algorithms
- The algorithms implement inference as latent steering followed by token generation, and training as minibatch steering, loss estimation, and weight updates.Energy steering is described as the step that enables EBFM to capture in-context dependency for generating new tokens.Source: §3.3 · pp. 4–5
4 Evaluation Results
The evaluation covers pre-training, supervised fine-tuning, zero-shot question-answering, and zero-shot generation. Across these settings, EBFMs show favorable scaling and outperform GPTs on reported comparisons.
4 Evaluation Results
- The evaluation spans pre-training, fine-tuning, QA, and generation, comparing EBFMs with established foundation architectures including GPTs and diffusion language models.The QA evaluation covers 19 tasks, while zero-shot generation covers seven downstream datasets.Source: §4 · p. 5, §4.1 · pp. 5–6
4.1 Pre-training and Evaluation Recipe
- Pre-training uses Fineweb, Climbmix, and DCLM, with models varying in depth and jointly scaling depth and hidden width.The reported depths are 12, 20, and 26 layers; scaling-law evaluation measures validation BPB across the three corpora.Source: §4.1 · p. 5
4.2 Evaluation of Scaling Law
- Across ClimbMix, DCLM, and Fineweb, EBFMs achieve lower BPB than GPTs at equal training-token counts across model sizes, while their BPB decreases more rapidly as training tokens increase.On DCLM, as training tokens increase from 65M to 10B, EBFMs' validation error decreases faster than GPTs, so their performance advantage grows.Source: §4.2 · p. 6
- As model size rises from 268M to 1.03B parameters, EBFM validation error decreases faster than GPT’s on DCLM at 5B and 10B training tokens.The reported performance advantage becomes more pronounced at larger model scales.Source: §4.2 · p. 7, Figure 5 · p. 7
- At fixed training FLOPs, EBFM’s IsoFLOP curves have U-shaped validation-BPB profiles, with compute-optimal model size shifting larger as budgets increase.The curve minima identify compute-optimal model sizes, and the minima shift down and right with increased budgets.Source: §4.2 · p. 7, Figure 5 · p. 7
4.3 Evaluation of Supervised Fine-tuning
- After starting from optimal pre-training checkpoints, EBFMs reach lower validation error with fewer SFT tokens than GPTs for d20 and d26 models.SFT uses Nanochat-SFT, a mixture of SmolTalk, MMLU, and GSM8K.Source: Figure 6 · p. 7, §4.3 · p. 8
4.4 Evaluation of Question-Answering Tasks
- On 19 zero-shot QA tasks, EBFMs significantly outperform GPTs on most tasks, and accuracy improves faster with model size and training tokens.The reported advantage grows at larger scales.Source: §4.4 · p. 8, Table 2 · p. 8, Figure 7 · p. 8
4.5 Zero-shot Generation
- In zero-shot generation, EBFMs achieve lower BPB than GPTs and MDLMs, by 0.02 and 0.09, respectively.EBFMs and GPTs train for one epoch with 9.06B tokens; MDLMs train for two epochs with 18.12B tokens.Source: §4.5 · p. 9, Table 3 · p. 9
5 Open-source Release
The authors release training infrastructure, logs, model weights, and links to the publicly available datasets to support reproduction and further exploration.
5 Open-source Release
- Training infrastructure, training logs, and model weights are released, and the publicly available datasets are linked in the released code.The authors invite the community to reproduce the results and explore energy-based foundation models.Source: §5 · p. 9
6 Limitations under Current Ecosystem
Current infrastructure constrains EBFM scale, while block-wise generation remains an important direction for extending energy steering to longer spans.
6 Limitations under Current Ecosystem
- Second-order differentiation and the lack of model sharding limit EBFM size to the memory capacity of a single H200 GPU.The largest configuration is d26, with 1.03B parameters; existing model-parallel systems do not yet readily support the required differentiation.Source: §6 · p. 9
- Block-wise generation remains future work, though jointly optimizing whole sentences could assess coherence and safety constraints over longer spans.The passage contrasts this with token-wise generation, which chooses a locally optimal token at each step.Source: §6 · p. 9
7 Conclusion
The paper presents EBFMs as a foundation architecture and examines their scaling across general-purpose tasks. It reports promising scaling performance and identifies potential for general-purpose use.
7 Conclusion
- EBFMs steer hidden states during inference toward lower-energy outputs to guide token generation.The paper also develops parallel energy steering for pre-training.Source: §7 · p. 9
- Across 19 downstream tasks, EBFMs show promising scaling performance as model size and training cost increase.The models were further pre-trained on three large-scale corpora.Source: §7 · p. 9
- The authors release infrastructure and model weights to enable full reproduction of their results.Source: §7 · p. 9
8 Future Directions
The authors invite further study of EBFMs and identify fine-tuning, efficiency, and training infrastructure as directions for development.
8 Future Directions
- Fine-tuning on domain-specific instructions and data could strengthen EBFMs for specialized areas such as scientific reasoning, code generation, and finance.The passage presents improved adaptability to diverse applications as a potential benefit.Source: §8 · p. 10
- Architecture-specific efficient inference and training algorithms are needed because existing GPT-oriented methods are difficult to apply to EBFMs.Source: §8 · p. 10
- Distributed infrastructure supporting multi-GPU sharding and memory-efficient higher-order gradients is proposed to scale EBFM training.Source: §8 · p. 10