Asia
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
RL is essentially distinct from and probably harder than that in standard offline RL.
Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu
Typically, preference optimization is approached as an offline supervised learning task using manually crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains under-explored.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation Y uanxin Liu, Lei Li
Recently, open-domain text-to-video (T2V) generation models have made remarkable progress.
Benchmark Data Repositories for Better Benchmarking
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets.
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities.