Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Offline Behavioral Data Selection

Shiye Lei, Zhihao Cheng, Dacheng Tao

TL;DR

The paper identifies data saturation in offline behavioral cloning, where policy performance quickly plateaus with dataset size, and attributes this to weak alignment between test loss and true performance under distribution shift. It introduces Stepwise Dual Ranking (SDR), a lightweight subset selection method using stepwise clipping and dual ranking to assemble a compact, informative training set that yields near-full-data performance with a fraction of the data. Theoretical analysis links per-step data allocation to policy performance and demonstrates SDR’s effectiveness through extensive experiments on the D4RL benchmark, with ablations confirming the value of both components. Overall, SDR offers a scalable, training-free approach to data selection that significantly improves data efficiency for offline BC in large-scale datasets.

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.

Offline Behavioral Data Selection

TL;DR

The paper identifies data saturation in offline behavioral cloning, where policy performance quickly plateaus with dataset size, and attributes this to weak alignment between test loss and true performance under distribution shift. It introduces Stepwise Dual Ranking (SDR), a lightweight subset selection method using stepwise clipping and dual ranking to assemble a compact, informative training set that yields near-full-data performance with a fraction of the data. Theoretical analysis links per-step data allocation to policy performance and demonstrates SDR’s effectiveness through extensive experiments on the D4RL benchmark, with ablations confirming the value of both components. Overall, SDR offers a scalable, training-free approach to data selection that significantly improves data efficiency for offline BC in large-scale datasets.

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.

Paper Structure

This paper contains 16 sections, 5 theorems, 6 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Preliminaries
  4. Offline Behavioral Data Selection
  5. Data Saturation in Offline BC Data
  6. Analysis of Data Saturation
  7. Algorithm
  8. Theoretical Analysis
  9. Stepwise Dual Ranking
  10. Experiments
  11. Main Results
  12. Ablation Study
  13. Hyperparameter Analysis
  14. Discussion and Limitation
  15. Conclusion
  16. ...and 1 more sections

Key Result

Theorem 1

For policies $\pi^\ast$ and ${\pi}$, we have $\left \vert J(\pi) - J(\pi^\ast) \right \vert \leq \epsilon T^2$, where $\epsilon = \mathbb{E}_{d_{\pi^*}}\left[D_{\operatorname{TV}}\left(\pi, \pi^*\right)\right]$.

Figures (5)

  • Figure 1: The workflow of Stepwise Dual Ranking. The original dataset is processed through stepwise progressive clip and dual ranking to construct a candidate pool. The final informative subset is then randomly selected from this candidate pool.
  • Figure 2: Plots of policy performance (normalized return) as functions of sample size. Each point is averaged over ten trials.
  • Figure 3: (a) $\texttt{P@50}$ and $\texttt{P@90}$ for offline BC datasets and image datasets (ImageNet, CIFAR-10). (b) Plots of policy performance (normalized return) as functions of the loss w.r.t. real data. Each point is calculated and then averaged over five trials.
  • Figure 4: Comparison of SDR and Random Selection (Rand) under different sample size. Each point is calculated and then averaged over ten trials.
  • Figure 5: Plots of policy performance as functions of $\lambda$. Each point is calculated and then averaged over ten trials.

Theorems & Definitions (6)

  • Theorem 1: ross2010efficient
  • Theorem 2
  • Lemma 1: ross2010efficient
  • Lemma 2
  • Theorem 3
  • Remark 1