Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design

• URL: http://arxiv.org/abs/2605.30610v1
• Date: Thu, 28 May 2026 22:02:57 GMT
• Title: Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design
• Abstract summary: We present a framework for Constrained Generative Optimization (CFO)<n>CFO balances reward and constraint satisfaction by reducing the original problem to sequential fine-tuning.<n>We provide convergence guarantees for constrained generative optimization and constrained generation via CFO.
• Score: 31.59334876687123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g., molecular synthesizability) is fundamental for their adoption in real-world scientific discovery applications such as molecular design or protein engineering. While recent works have introduced scalable methods for reward-guided fine-tuning of such models via reinforcement learning and control schemes, it remains an open problem how to algorithmically trade-off reward maximization and constraint satisfaction in a reliable and predictable manner. Motivated by this challenge, we first present a rigorous framework for Constrained Generative Optimization, which brings an optimization viewpoint to the introduced adaptation problem and retrieves the relevant task of constrained generation as a sub-case. Then, we introduce Constrained Flow Optimization (CFO), an algorithm that automatically and provably balances reward maximization and constraint satisfaction by reducing the original problem to sequential fine-tuning via established, scalable methods. We provide convergence guarantees for constrained generative optimization and constrained generation via CFO. Ultimately, we present an experimental evaluation of CFO on both synthetic, yet illustrative, settings, and a molecular design task. Across these evaluations, CFO achieves consistent increases in reward while ensuring high constraint satisfaction, showcasing its practical utility for constrained generative optimization.

Related papers

• CVaR-Assisted Custom Penalty Function for Constrained Optimization [3.652509571098291]
  Constrained optimization problems are frequently reformulated as quadratic unconstrained binary optimization (QUBO) models.<n>Standard QUBO formulations enforce inequality constraints through slack variables and quadratic penalties.<n>We propose a slack-free penalty formulation for constrained binary optimization that eliminates auxiliary slack variables.
  arXiv  Detail & Related papers  (2026-04-22T01:08:38Z)
• Constraint-Aware Generative Re-ranking for Multi-Objective Optimization in Advertising Feeds [3.791872390898376]
  We propose a constraint-aware generative reranking framework that transforms constrained optimization into bounded neural decoding.<n>Our framework unifies sequence generation and reward estimation into a single network.<n> Experiments on large-scale industrial feeds and online A/B tests show that our method improves revenue and user engagement while meeting strict latency requirements.
  arXiv  Detail & Related papers  (2026-03-04T16:09:36Z)
• Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning [59.11663802446183]
  Flow and diffusion generative models can be adapted to optimize task-specific objectives while preserving prior information.<n>We introduce Flow Density Control (FDC), a simple algorithm that reduces this complex problem to a specific sequence of simpler fine-tuning tasks.<n>We derive convergence guarantees for the proposed scheme under realistic assumptions by leveraging recent understanding of mirror flows.
  arXiv  Detail & Related papers  (2025-11-27T17:19:01Z)
• TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making [75.29820290660065]
  This paper proposes Thought-Centric Preference Optimization ( TCPO) for effective embodied decision-making.<n>It emphasizes the alignment of the model's intermediate reasoning process, mitigating the problem of model degradation.<n>Experiments in the ALFWorld environment demonstrate an average success rate of 26.67%, achieving a 6% improvement over RL4VLM.
  arXiv  Detail & Related papers  (2025-09-10T11:16:21Z)
• Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning [132.7040981721302]
  We study the Constrained Convex Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure. Design algorithms for a constrained convex MDP faces several challenges, including handling the large state space.
  arXiv  Detail & Related papers  (2024-02-16T16:35:18Z)
• Pseudo-Bayesian Optimization [7.556071491014536]
  We study an axiomatic framework that elicits the minimal requirements to guarantee black-box optimization convergence.<n>We show how using simple local regression, and a suitable "randomized prior" construction to quantify uncertainty, not only guarantees convergence but also consistently outperforms state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2023-10-15T07:55:28Z)
• No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations [0.0]
  Constrained Upper Quantile Bound (CUQB) is a conceptually simple, deterministic approach that avoids constraint approximations. We show that CUQB significantly outperforms traditional Bayesian optimization in both constrained and unconstrained cases.
  arXiv  Detail & Related papers  (2023-05-05T19:57:36Z)
• Conditional Denoising Diffusion for Sequential Recommendation [62.127862728308045]
  Two prominent generative models, Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. We present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser.
  arXiv  Detail & Related papers  (2023-04-22T15:32:59Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process [56.55075925645864]
  The problem of constrained decision process (CMDP) is investigated, where an agent aims to maximize the expected accumulated discounted reward subject to multiple constraints. A new utilities-dual convex approach is proposed with novel integration of three ingredients: regularized policy, dual regularizer, and Nesterov's gradient descent dual. This is the first demonstration that nonconcave CMDP problems can attain the lower bound of $mathcal O (1/epsilon)$ for all complexity optimization subject to convex constraints.
  arXiv  Detail & Related papers  (2021-10-20T02:57:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.