Offline Behavioral Data Selection

• URL: http://arxiv.org/abs/2512.18246v1
• Date: Sat, 20 Dec 2025 07:10:58 GMT
• Title: Offline Behavioral Data Selection
• Authors: Shiye Lei, Zhihao Cheng, Dacheng Tao,
• Abstract summary: We show that policy performance rapidly saturates when trained on a small fraction of the dataset.<n>We propose a simple yet effective method, Stepwise Dual Ranking (SDR), which extracts a compact yet informative subset from large-scale offline behavioral datasets.<n>Extensive experiments and ablation studies on D4RL benchmarks demonstrate that SDR significantly enhances data selection for offline behavioral data.
• Score: 58.116300485427764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Behavioral cloning is a widely adopted approach for offline policy learning from expert demonstrations. However, the large scale of offline behavioral datasets often results in computationally intensive training when used in downstream tasks. In this paper, we uncover the striking data saturation in offline behavioral data: policy performance rapidly saturates when trained on a small fraction of the dataset. We attribute this effect to the weak alignment between policy performance and test loss, revealing substantial room for improvement through data selection. To this end, we propose a simple yet effective method, Stepwise Dual Ranking (SDR), which extracts a compact yet informative subset from large-scale offline behavioral datasets. SDR is build on two key principles: (1) stepwise clip, which prioritizes early-stage data; and (2) dual ranking, which selects samples with both high action-value rank and low state-density rank. Extensive experiments and ablation studies on D4RL benchmarks demonstrate that SDR significantly enhances data selection for offline behavioral data.

Related papers

• Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward [54.708851958671794]
  We propose a Data-Efficient Policy Optimization pipeline that combines optimized strategies for both offline and online data selection.<n>In offline phase, we curate a high-quality subset of training samples based on diversity, influence, and appropriate difficulty.<n>During online RLVR training, we introduce a sample-level explorability metric to dynamically filter samples with low exploration potential.
  arXiv  Detail & Related papers  (2025-09-01T10:04:20Z)
• Goal-Conditioned Data Augmentation for Offline Reinforcement Learning [9.181158786602085]
  We introduce Goal-cOnditioned Data Augmentation (GODA), a goal-conditioned diffusion-based method for augmenting samples with higher quality.<n>GODA learns a comprehensive distribution representation of the original offline datasets while generating new data with selectively higher-return goals.<n>We conduct experiments on the D4RL benchmark and real-world challenges, specifically traffic signal control (TSC) tasks, to demonstrate GODA's effectiveness.
  arXiv  Detail & Related papers  (2024-12-29T16:42:30Z)
• Offline Reinforcement Learning from Datasets with Structured Non-Stationarity [50.35634234137108]
  Current Reinforcement Learning (RL) is often limited by the large amount of data needed to learn a successful policy. We address a novel Offline RL problem setting in which, while collecting the dataset, the transition and reward functions gradually change between episodes but stay constant within each episode. We propose a method based on Contrastive Predictive Coding that identifies this non-stationarity in the offline dataset, accounts for it when training a policy, and predicts it during evaluation.
  arXiv  Detail & Related papers  (2024-05-23T02:41:36Z)
• Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets [53.8218145723718]
  offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. We argue that when a dataset is dominated by suboptimal trajectories, state-of-the-art offline RL algorithms do not substantially improve over the average return of trajectories in the dataset. We present a realization of the sampling strategy and an algorithm that can be used as a plug-and-play module in standard offline RL algorithms.
  arXiv  Detail & Related papers  (2023-10-06T17:58:14Z)
• Boosting Offline Reinforcement Learning via Data Rebalancing [104.3767045977716]
  offline reinforcement learning (RL) is challenged by the distributional shift between learning policies and datasets. We propose a simple yet effective method to boost offline RL algorithms based on the observation that resampling a dataset keeps the distribution support unchanged. We dub our method ReD (Return-based Data Rebalance), which can be implemented with less than 10 lines of code change and adds negligible running time.
  arXiv  Detail & Related papers  (2022-10-17T16:34:01Z)
• DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning [17.664027379555183]
  offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. This paper formulates the offline dynamics adaptation by using (source) offline data collected from another dynamics to relax the requirement for the extensive (target) offline data. With only modest amounts of target offline data, our performance consistently outperforms the prior offline RL methods in both simulated and real-world tasks.
  arXiv  Detail & Related papers  (2022-03-13T14:30:55Z)
• The Challenges of Exploration for Offline Reinforcement Learning [8.484491887821473]
  We study the two processes of reinforcement learning: collecting informative experience and inferring optimal behaviour. The task-agnostic setting for data collection, where the task is not known a priori, is of particular interest. We use this decoupled framework to strengthen intuitions about exploration and the data prerequisites for effective offline RL.
  arXiv  Detail & Related papers  (2022-01-27T23:59:56Z)
• Representation Matters: Offline Pretraining for Sequential Decision Making [27.74988221252854]
  In this paper, we consider a slightly different approach to incorporating offline data into sequential decision-making. We find that the use of pretraining with unsupervised learning objectives can dramatically improve the performance of policy learning algorithms.
  arXiv  Detail & Related papers  (2021-02-11T02:38:12Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)

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.