4 citations · 5 across the 10 of their papers we have counts for
11 papers
GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer
Junmo Cho, Suhan Kim, Sangjune An +5
Finding effective prompts for language models (LMs) is critical yet notoriously difficult: the prompt space is combinatorially large, rewards are sparse due to expensive target-LM…
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim, Minsu Kim, Celine Roget +5
The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in pract…
PhysicsFC: Learning User-Controlled Skills for a Physics-Based Football Player Controller
Minsu Kim, Eunho Jung, Yoonsang Lee
We propose PhysicsFC, a method for controlling physically simulated football player characters to perform a variety of football skills--such as dribbling, trapping, moving, and kic…
Self-Evolving Curriculum for LLM Reasoning
Xiaoyin Chen, Jiarui Lu, Minsu Kim +6
Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abilities in domains such as mathematics and…
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee, Truong Nhat Nguyen Bao, Jaesik Yoon +4
Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling metho…
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…
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training
Brian Bartoldson, Siddarth Venkatraman, James Diffenderfer +7
Reinforcement learning (RL) is a critical component of large language model (LLM) post-training. However, on-policy algorithms used for post-training are not naturally robust to a…
Offline Model-Based Optimization: Comprehensive Review
Minsu Kim, Jiayao Gu, Ye Yuan +4
Offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets. This setting is particula…
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}…
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…
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee, Minsu Kim, Lynn Cherif +8
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing ef…