Autoregressive Boltzmann Generators (ArBG) achieve 60%+ improvement in molecular equilibrium sampling
Researchers introduce Autoregressive Boltzmann Generators (ArBG), a framework that replaces normalizing flows in Boltzmann Generators with autoregressive models inspired by LLM architectures, circumventing invertibility constraints and enabling sequential inference-time interventions. The approach demonstrates significant improvements over flow-based models across benchmarks, particularly on larger peptide systems like the 10-residue Chignolin. The authors also release Robin, a 132M-parameter transferable model trained with ArBG that reduces zero-shot energy error on 8-residue systems by over 60% compared to prior state-of-the-art.
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Implicit Generation and Generalization Methods for Energy-Based Models
OpenAI published research on stable and scalable training of energy-based models (EBMs), achieving sample quality competitive with GANs at low temperatures while retaining mode coverage guarantees of likelihood-based models. The approach uses iterative compute during generation to continually refine outputs. This work positions EBMs as a promising alternative generative modeling paradigm bridging GANs and likelihood-based models.
SURGE: Approximation-free Training-Free Particle Filter for Diffusion Surrogate
The paper introduces URGE (Unbiased Resampling via Girsanov Estimation), a derivative-free inference-time scaling algorithm for diffusion models that performs path-wise importance reweighting using a Girsanov change of measure. Unlike existing inference-time guidance methods, URGE requires no score, Hessian, or PDE evaluations, attaching multiplicative weights to simulated trajectories and periodically resampling. The authors establish a theoretical equivalence between path-wise and particle-wise sequential Monte Carlo (SMC), guaranteeing unbiased terminal distributions. Empirically, URGE outperforms existing inference-time guidance baselines on synthetic tests and diffusion-model benchmarks while being simpler to implement.
Kolmogorov Regression lifts diffusion policies to Cameron-Martin space for robust long-horizon control
Researchers introduce a backward Kolmogorov equation framework that reformulates diffusion policy training as a deterministic boundary-value PDE problem in Cameron-Martin space, replacing stochastic score matching. The approach uses a precision-weighted Cameron-Martin loss and a Kolmogorov residual as an inference-time failure detector, yielding convergence guarantees tied to kernel effective rank rather than action dimension. Validation on the PushT manipulation benchmark shows 17% improvement in episode reward and 67.6% reduction in inter-step drift; a 6-station manufacturing scheduling task shows 28.4% lower RMSE than LSTM baselines and 96% reduction in deadlock events via Hamilton-Jacobi reachability certification.
FMLM+ introduces Posterior Refinement for fast non-autoregressive language generation
Researchers introduce FMLM+, a framework combining Flow Map Language Models with masking-style noise schedules to enable joint sequence generation with per-token global consistency scoring. The key contribution is Posterior Refinement, an inference-time self-correction strategy that matches discrete baseline performance with 32x fewer neural function evaluations (NFEs). The approach improves the speed-quality tradeoff over both Masked Diffusion Models and standard FLMMs across multiple benchmarks, addressing longstanding factorization error problems in non-autoregressive generation.
GGRO: Gradient-Guided Reward Optimization for inference-time LLM alignment
Researchers introduce Gradient-Guided Reward Optimization (GGRO), an inference-time alignment method that uses gradient signals from a reward model to inject 'nudging tokens' at high-uncertainty decoding steps, rather than relying on sampling-intensive re-ranking approaches like Best-of-N. The method monitors token-level entropy to detect distribution drift and steers generation trajectories directly, claiming improved robustness to reward hacking with minimal computational overhead. Experiments show gains across safety, helpfulness, and reasoning benchmarks compared to standard inference-time alignment baselines.
FlowPipe: LLM-conditioned Generative Flow Networks for automated data preparation pipeline construction
FlowPipe is a new framework that frames ML data preparation pipeline synthesis as conditional probabilistic flow generation over a directed acyclic graph, using Conditional Generative Flow Networks (C-GFlowNets) with a Trajectory Balance objective. LLM-derived semantic priors are injected into the policy via Feature-wise Linear Modulation (FiLM), and a failure-aware flow objective steers search away from invalid states. Evaluated on 74 real-world datasets across two benchmark suites, FlowPipe improves accuracy by 11.96% on average over SOTA baselines and achieves 12.5x faster training convergence. The work addresses long-standing limitations in automated data pipeline construction including weak credit assignment and inefficient exploration.
GADD: Gibbs-Accelerated Discrete Diffusion Achieves Polylog Sampling Complexity
This paper introduces Gibbs-Accelerated Discrete Diffusion (GADD), a corrector method for uniform-rate discrete diffusion models that constructs Gibbs posterior likelihoods directly from the concrete score function without additional training. GADD achieves O(polylog(ε⁻¹)) sampling complexity, the first such rate for diffusion-based samplers in this setting. Experiments on synthetic data, zero-shot text sampling, and zero-shot conditional music generation show consistent improvements in sample quality and wall-clock efficiency over Euler and CTMC baselines. The work also introduces a novel induction-based theoretical framework for analyzing predictor-corrector methods in discrete diffusion.
Glow: Better reversible generative models
OpenAI introduces Glow, a reversible generative model using invertible 1x1 convolutions that extends prior work on normalizing flows. The model generates realistic high-resolution images, supports efficient sampling, and learns disentangled features for attribute manipulation. Code and an online visualization tool are released alongside the paper.