Dinghuai Zhang

Ehsan Kamalloo

2025-05-20

ArXiv (prépublication)

Self-Evolving Curriculum for LLM Reasoning

Minsu Kim

Alexandre Piché

Nicolas Gontier

Ehsan Kamalloo

2025-05-01

arXiv (publié)

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets

Zhen Liu

Tim Z. Xiao

Weiyang Liu

While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetun… (voir plus)e pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. Inspired by recent successes in generative flow networks (GFlowNets), a class of probabilistic models that sample with the unnormalized density of a reward function, we propose a novel GFlowNet method dubbed Nabla-GFlowNet (abbreviated as

2025-01-22

ICLR.cc/2025/Conference (poster)

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets

Zhen Liu

Tim Z. Xiao

Weiyang Liu

While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetun… (voir plus)e pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. Inspired by recent successes in generative flow networks (GFlowNets), a class of probabilistic models that sample with the unnormalized density of a reward function, we propose a novel GFlowNet method dubbed Nabla-GFlowNet (abbreviated as

2024-12-10

ArXiv (prépublication)

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets

Zhen Liu

Tim Z. Xiao

Weiyang Liu

While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetun… (voir plus)e pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. Inspired by recent successes in generative flow networks (GFlowNets), a class of probabilistic models that sample with the unnormalized density of a reward function, we propose a novel GFlowNet method dubbed Nabla-GFlowNet (abbreviated as

2024-12-10

ArXiv (prépublication)

EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics

Chenqing Hua

Yang Liu

Odin Zhang

Kevin K Yang

Shuangjia Zheng

2024-10-13

NeurIPS.cc/2024/Workshop/AIDrugX (poster)

Estimating Expectations without Sampling: Neural Stein Estimation

Mohsin Hasan

Cheikh Ahmed

Awa Khouna

We propose a method for estimating the expected value of a given function …

2024-05-27

approximateinference.org/AABI/2024/Symposium (accepté)

Learning to Scale Logits for Temperature-Conditional GFlowNets

Minsu Kim

Joohwan Ko

Ling Pan

Taeyoung YUN

Woo Chang Kim

Jinkyoo Park

Emmanuel Bengio

GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, tempera… (voir plus)ture-conditional GFlowNets can introduce temperature-based controllability for exploration and exploitation. We propose \textit{Logit-scaling GFlowNets} (Logit-GFN), a novel architectural design that greatly accelerates the training of temperature-conditional GFlowNets. It is based on the idea that previously proposed approaches introduced numerical challenges in the deep network training, since different temperatures may give rise to very different gradient profiles as well as magnitudes of the policy's logits. We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. Also, using Logit-GFN, GFlowNets can be improved by having better generalization capabilities in offline learning and mode discovery capabilities in online learning, which is empirically verified in various biological and chemical tasks. Our code is available at https://github.com/dbsxodud-11/logit-gfn

2024-05-01

ICML.cc/2024/Conference (poster)

Distributional GFlowNets with Quantile Flows

Ling Pan

Ricky T. Q. Chen

Aaron Courville

Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating com… (voir plus)plex combinatorial structure through a series of decision-making steps. Despite being inspired from reinforcement learning, the current GFlowNet framework is relatively limited in its applicability and cannot handle stochasticity in the reward function. In this work, we adopt a distributional paradigm for GFlowNets, turning each flow function into a distribution, thus providing more informative learning signals during training. By parameterizing each edge flow through their quantile functions, our proposed \textit{quantile matching} GFlowNet learning algorithm is able to learn a risk-sensitive policy, an essential component for handling scenarios with risk uncertainty. Moreover, we find that the distributional approach can achieve substantial improvement on existing benchmarks compared to prior methods due to our enhanced training algorithm, even in settings with deterministic rewards.

2024-02-16

TMLR (accepté)

Delta-AI: Local objectives for amortized inference in sparse graphical models

Jean-Pierre R. Falet

Hae Beom Lee

Chen Sun

We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call …

2024-01-16

ICLR.cc/2024/Conference (poster)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization

Ricky T. Q. Chen

Cheng-Hao Liu

Aaron Courville

2024-01-16

ICLR.cc/2024/Conference (poster)

Local Search GFlowNets

Minsu Kim

Sungsoo Ahn

Jinkyoo Park

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. 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: https://github.com/dbsxodud-11/ls_gfn.

2024-01-16

ICLR.cc/2024/Conference (spotlight)