Pith. sign in

REVIEW 5 cited by

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2201.13425 v3 pith:MWBD3BWK submitted 2022-01-31 cs.LG cs.AI

Denis Yarats , David Brandfonbrener , Hao Liu , Michael Laskin , Pieter Abbeel , Alessandro Lazaric , Lerrel Pinto This is my paper
classification cs.LGcs.AI
keywords dataofflineexorlexploratorylearningalgorithmschangedownstream
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Recent progress in deep learning has relied on access to large and diverse datasets. Such data-driven progress has been less evident in offline reinforcement learning (RL), because offline RL data is usually collected to optimize specific target tasks limiting the data's diversity. In this work, we propose Exploratory data for Offline RL (ExORL), a data-centric approach to offline RL. ExORL first generates data with unsupervised reward-free exploration, then relabels this data with a downstream reward before training a policy with offline RL. We find that exploratory data allows vanilla off-policy RL algorithms, without any offline-specific modifications, to outperform or match state-of-the-art offline RL algorithms on downstream tasks. Our findings suggest that data generation is as important as algorithmic advances for offline RL and hence requires careful consideration from the community. Code and data can be found at https://github.com/denisyarats/exorl .

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Diffusion Guidance Is a Controllable Policy Improvement Operator

    cs.LG 2025-05 conditional novelty 7.0 of 10

    Diffusion guidance with a tunable weight is a controllable policy improvement operator, improving offline and goal-conditioned policies beyond the data without retraining and often without a value function.

  2. Dichotomous Diffusion Policy Optimization

    cs.LG 2025-12 conditional novelty 5.0 of 10

    DIPOLE decomposes a KL-regularized RL objective into a pair of sigmoid-weighted diffusion policies whose score combination (CFG-like) yields stable and controllable policy improvement.

  3. Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies

    cs.LG 2025-09 conditional novelty 5.0 of 10

    Offline-trained world-model agents in DreamerV3 underperform online agents due to out-of-distribution states at test time; adding about 10% self-generated data or exploratory data largely recovers performance.

  4. Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies

    cs.LG 2025-08 conditional novelty 4.0 of 10

    A survey that categorizes deep reinforcement learning scaling strategies into data, network, and training budget dimensions and outlines challenges for scaling DRL systems.

  5. Reinforcement Learning: From Algorithms To Foundation Models

    cs.AI 2026-07 conditional novelty 3.0 of 10

    A dissertation uniting the author's published results: non-exploitable Nash-DQN policies and the FightLadder benchmark for games, plus diffusion/consistency-model world models for RL — a compilation rather than new results.

Pith tools