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Showing papers from Imperial College London / Mila Show all papers

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NeurIPS 2026Imperial College LondonImperial College London / MilaOffline RL

Aligning Flow Map Policies with Optimal $Q$-Guidance

Flow map policies learn multi-step jumps across flow dynamics for fast action generation, and FMQ adapts them via optimal closed-form Q-guidance to achieve state-of-the-art offline-to-online RL with 21.3% higher success rates.

Christos Ziakas, Alessandra Russo, Joey Bose

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
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NeurIPS 2026SpotlightGenesis Molecular AIMITMassachusetts Institute of TechnGenesis TherapeuticsGenesis Molecular AIFlow matching

Few-Step Cofolding with All-Atom Flow Maps

DeCAF distills all-atom biomolecular cofolding diffusion models into few-step flow maps with SE(3)-aligned endpoint losses, improving accuracy and physical validity at strict inference budgets.

Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam and 6 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 3/5
medium 10/10
strict 1/5
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NeurIPS 2026DukeImperial College London / MilaAithyraDiffusion models

Coupling Models for One-Step Discrete Generation

Coupling Models learn direct couplings between discrete sequences and Gaussian latents for one-step generation, reducing LM1B perplexity by 33%, Fly Brain FBD by 18%, and MNIST-Binary FID by 46%.

Fred Peng, Joey Bose, Anru Zhang, Alexander Tong

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5