Local Search GFlowNets

• URL: http://arxiv.org/abs/2310.02710v2
• Date: Fri, 22 Mar 2024 18:49:46 GMT
• Title: Local Search GFlowNets
• Authors: Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park,
• Abstract summary: Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue.
• Score: 85.0053493167887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: \url{https://github.com/dbsxodud-11/ls_gfn}.

Related papers

• Embarrassingly Parallel GFlowNets [17.751086304612667]
  GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. EP-GFlowNet is a provably correct divide-and-conquer method to sample from product distributions of the form $R(cdot) propto R_1(cdot)... R_N(cdot)$ -- e.g., in parallel or federated Bayes.
  arXiv  Detail & Related papers  (2024-06-05T13:59:05Z)
• Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets [27.33222647437964]
  Generative Flow Networks (GFlowNets) are amortized sampling methods for learning a policy to sequentially generate objects with probabilities to their rewards. GFlowNets exhibit a remarkable ability to generate diverse sets of high-reward proportional objects, in contrast to standard reinforcement learning approaches. Recent works have arisen for learning goal-conditioned GFlowNets to acquire various useful properties, aiming to train a single GFlowNet capable of achieving different goals as the task specifies. We propose a novel method named Retrospective Backward Synthesis (RBS) to address these challenges. Specifically, RBS synthesizes a new backward trajectory
  arXiv  Detail & Related papers  (2024-06-03T09:44:10Z)
• Stochastic Generative Flow Networks [89.34644133901647]
  Generative Flow Networks (or GFlowNets) learn to sample complex structures through the lens of "inference as control" Existing GFlowNets can be applied only to deterministic environments, and fail in more general tasks with dynamics. This paper introduces GFlowNets, a new algorithm that extends GFlowNets to environments.
  arXiv  Detail & Related papers  (2023-02-19T03:19:40Z)
• GFlowOut: Dropout with Generative Flow Networks [76.59535235717631]
  Monte Carlo Dropout has been widely used as a relatively cheap way for approximate Inference. Recent works show that the dropout mask can be viewed as a latent variable, which can be inferred with variational inference. GFlowOutleverages the recently proposed probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks.
  arXiv  Detail & Related papers  (2022-10-24T03:00:01Z)
• Generative Augmented Flow Networks [88.50647244459009]
  We propose Generative Augmented Flow Networks (GAFlowNets) to incorporate intermediate rewards into GFlowNets. GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration.
  arXiv  Detail & Related papers  (2022-10-07T03:33:56Z)
• Learning GFlowNets from partial episodes for improved convergence and stability [56.99229746004125]
  Generative flow networks (GFlowNets) are algorithms for training a sequential sampler of discrete objects under an unnormalized target density. Existing training objectives for GFlowNets are either local to states or transitions, or propagate a reward signal over an entire sampling trajectory. Inspired by the TD($lambda$) algorithm in reinforcement learning, we introduce subtrajectory balance or SubTB($lambda$), a GFlowNet training objective that can learn from partial action subsequences of varying lengths.
  arXiv  Detail & Related papers  (2022-09-26T15:44:24Z)
• Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation [110.09855163856326]
  This paper is about the problem of learning a policy for generating an object from a sequence of actions. We propose GFlowNet, based on a view of the generative process as a flow network. We prove that any global minimum of the proposed objectives yields a policy which samples from the desired distribution.
  arXiv  Detail & Related papers  (2021-06-08T14:21:10Z)

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.