1 citations · 2 across the 7 of their papers we have counts for
7 papers
Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
Maciej Chrabąszcz, Aleksander Szymczyk, Marcin Sendera +2
Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not always faithful to the model's f…
Solving Bayesian inverse problems with diffusion priors and off-policy RL
Luca Scimeca, Siddarth Venkatraman, Moksh Jain +14
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically sol…
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman, Mohsin Hasan, Minsu Kim +5
Any well-behaved generative model over a variable can be expressed as a deterministic transformation of an exogenous ('outsourced') Gaussian noise variable $\mathbf{z}…
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
Julius Berner, Lorenz Richter, Marcin Sendera +2
We study the problem of training neural stochastic differential equations, or diffusion models, to sample from a Boltzmann distribution without access to target samples. Existing m…
Amortizing intractable inference in diffusion models for vision, language, and control
Siddarth Venkatraman, Moksh Jain, Luca Scimeca +12
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable…
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose +9
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem…
Improved off-policy training of diffusion samplers
Marcin Sendera, Minsu Kim, Sarthak Mittal +6
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark several diffusion-structured infe…