arxiv: 2604.25496 · v1 · submitted 2026-04-28 · 💻 cs.AI

Recognition: unknown

Improving Zero-Shot Offline RL via Behavioral Task Sampling

Nazim Bendib , Nicolas Perrin-Gilbert , Olivier Sigaud

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:22 UTC · model grok-4.3

classification 💻 cs.AI

keywords offline reinforcement learningzero-shot generalizationtask samplingreward function extractionbehavioral task vectorspolicy training distributionunseen rewards

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The pith

Extracting task vectors from the offline dataset instead of random sampling improves zero-shot RL performance by an average of 20 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that standard offline zero-shot RL trains policies on randomly chosen task vectors, which fails to match the actual structure of possible rewards and leads to weaker generalization on new tasks. It proposes instead to derive those vectors directly from the given offline data using a reward extraction step that then shapes the entire training distribution. This change integrates into existing algorithms without extra environment steps. A reader would care because it turns limited behavioral records into a more effective basis for handling goals that appear only after training ends.

Core claim

The central claim is that replacing random task-vector sampling with a simple reward-function extraction procedure applied to the offline dataset produces a task distribution that yields stronger zero-shot generalization to unseen linear reward functions, delivering roughly 20 percent higher average performance across standard benchmark environments and multiple baseline algorithms.

What carries the argument

The reward function extraction procedure, which pulls task vectors straight from the offline dataset to define the training task distribution instead of drawing them at random.

If this is right

Existing offline zero-shot RL methods gain performance simply by swapping their task-sampling step for the extraction procedure.
Zero-shot policies generalize better to linear reward functions that never appeared during training.
The performance lift appears consistently across several benchmark environments and baseline algorithms.
Principled choice of the training task distribution matters more than the implicit assumption of random sampling allows.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same extraction idea could be tested on datasets that contain only partial coverage of the state space to check whether it still supplies useful task vectors.
If the procedure works for linear rewards, it raises the question of whether a similar extraction step can be defined for nonlinear reward functions without changing the rest of the pipeline.
Connecting this sampling choice to other offline RL settings where task or goal distributions are chosen in advance might reveal shared principles about how behavioral data encodes useful structure.

Load-bearing premise

The extracted task vectors from the offline dataset capture the full structure of the task space without introducing selection bias or leaving gaps in coverage of possible unseen rewards.

What would settle it

Re-running the benchmark experiments with the extraction procedure turned off so that task vectors revert to random sampling, and finding no measurable drop in zero-shot success rates, would show the claimed improvement does not hold.

Figures

Figures reproduced from arXiv: 2604.25496 by Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud.

**Figure 1.** Figure 1: Zero-shot performance comparison across task sampling strategies. Results on Cheetah, Walker, and Quadruped for multiple representations show that BTD-sampling improves average performance and reduces sensitivity to representation choice. The horizontal red line indicates offline TD3 (oracle). • Self-supervised representation learning methods: BYOL (Grill et al., 2020) and BYOL-γ (Lawson et al., 2025) lear… view at source ↗

**Figure 2.** Figure 2: Average test performance and variability of all baseline methods (solid) against their BTD-sampling counterparts (hatched) across three environments. BTD-sampling consistently improves performance while significantly reducing variance, raising the lower bound of performance toward the offline TD3 oracle. ment of +20%. This gain is particularly substantial in the Cheetah environment and when using AEnc an… view at source ↗

**Figure 3.** Figure 3: Test performance across a range of latent task dimensions d. Baseline performance drops in high dimensions as task vectors become increasingly orthogonal to the behavioral space, diluting the training signal. In contrast, BTD-sampling maintains robust performance by learning a task distribution that stays within the behavioral space, ensuring a consistent and informative training signal. distributions are … view at source ↗

**Figure 4.** Figure 4: Test performance across Cheetah, Walker, and Quadruped as the interpolation parameter α varies from 0 (pure BTD-sampling) to 1 (pure uniform). Increasing the proportion of uniformly sampled tasks leads to performance degradation across almost all methods. This shows that uniform sampling dilutes the training signal rather than enhancing generalization. This confirms that aligning the task distribution with… view at source ↗

**Figure 5.** Figure 5: Signal dilution under increasing feature dimension. The normalized trace of the empirical feature-occupancy covariance (1/d)Tr(ΣΨ) decays rapidly as the feature dimension d increases across environments and baselines. Proposition 4.1 characterizes the decay of the expected return variance when the tasks are uniformly sampled. The theoretical quantity Ψ represents the set of all possible feature occupancies… view at source ↗

**Figure 6.** Figure 6: Effect of the number of GMM components on test performance. Performance improves rapidly with more components up to about 20, after which gains generally plateau across environments and baselines. Results: Across all environments and baseline methods, we observe a consistent trend: performance increases rapidly until approximately 20 components, after which gains diminish. This indicates that the behaviora… view at source ↗

**Figure 7.** Figure 7: UMAP visualization of task distributions. Comparison between empirical task vectors z ∼ pdata and uniformly sampled task vectors z ∼ Unif(S ( d − 1), showing limited overlap. 17 view at source ↗

read the original abstract

Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state representations. In most existing algorithms, these task vectors are randomly sampled, implicitly assuming this adequately captures the structure of the task space. We argue that doing so leads to suboptimal zero-shot generalization. To address this limitation, we propose extracting task vectors directly from the offline dataset and using them to define the task distribution used for policy training. We introduce a simple and general reward function extraction procedure that integrates into existing offline zero-shot RL algorithms. Across multiple benchmark environments and baselines, our approach improves zero-shot performance by an average of 20%, highlighting the importance of principled task sampling in offline zero-shot RL.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Extracting task vectors from the offline dataset beats random sampling for zero-shot RL, delivering reported 20% gains but with limited visible checks on bias or coverage.

read the letter

The main thing to know is that this paper replaces random task-vector sampling with vectors pulled from the offline data via a reward extraction step, and reports average 20% better zero-shot performance across benchmarks and baselines. The change is presented as a simple, general fix that slots into existing offline zero-shot RL methods without new interactions or heavy machinery. That is the core empirical claim and the part that feels most actionable on first read. The extraction procedure itself is the actual novelty; prior work apparently treated random sampling as sufficient, and this directly challenges that by tying the training distribution to observed behaviors in the dataset. The results are consistent enough in the abstract to suggest the idea has legs for domains where new rollouts are costly. No circularity or self-referential fitting shows up in the description, which is a plus. The soft spots sit mostly in the experimental support. The abstract states the gains but does not detail statistical tests, full ablations on the extraction step, or direct checks on whether the extracted vectors span the evaluation task space or just reweight toward data-collection behaviors. If the convex hull of those vectors is narrower than the space of possible linear rewards, the zero-shot improvement could be narrower than claimed. That matches the stress-test concern about subspace bias, and it is worth verifying in the full text rather than assuming the procedure is distributionally neutral. The paper is aimed at offline RL researchers who already work with task-conditioned policies and want a low-overhead sampling tweak. A reader running zero-shot experiments on new environments would get immediate practical value from trying the extraction step, even if the broader generalization story needs more data. It deserves a serious referee because the idea is clean, the reported lift is large enough to matter, and the method is reproducible enough to test quickly. I would send it to review rather than desk-reject, with the expectation that the authors supply the missing ablations and coverage analysis.

Referee Report

2 major / 2 minor

Summary. The paper claims that in offline zero-shot RL, randomly sampling task vectors for training task-conditioned policies is suboptimal, and proposes instead extracting task vectors directly from the offline dataset via a simple reward function extraction procedure. This change integrates into existing algorithms and yields an average 20% improvement in zero-shot performance across multiple benchmark environments and baselines.

Significance. If the empirical gains hold under rigorous validation, the work would be significant for highlighting that data-driven task sampling can outperform random sampling in capturing task-space structure for generalization to unseen linear rewards w · ϕ(s). It provides a lightweight, algorithm-agnostic modification that could be adopted broadly in offline RL pipelines.

major comments (2)

[Experiments] Experiments section: the reported average 20% improvement lacks accompanying details on run count, standard deviations, confidence intervals, or statistical significance tests, making it impossible to determine whether the gains are reliable or could be explained by variance.
[Method] Method section (reward extraction procedure): no analysis is provided showing that the extracted task vectors improve coverage of the evaluation task space (e.g., via convex-hull volume, principal-component span, or distance to unseen w vectors) rather than merely reweighting toward behaviors already present in the data-collection policy; without this, the 20% gain does not yet establish superior zero-shot generalization.

minor comments (2)

[Abstract] The abstract and introduction would benefit from a brief equation or pseudocode snippet illustrating the reward extraction step for immediate clarity.
[Experiments] Table captions and axis labels in the experimental figures should explicitly state the number of seeds and whether error bars represent standard error or deviation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment point by point below, indicating where revisions will be made to strengthen the paper.

read point-by-point responses

Referee: [Experiments] Experiments section: the reported average 20% improvement lacks accompanying details on run count, standard deviations, confidence intervals, or statistical significance tests, making it impossible to determine whether the gains are reliable or could be explained by variance.

Authors: We agree that these statistical details are necessary to establish the reliability of the results. The manuscript currently reports only the average improvement without run counts, standard deviations, confidence intervals, or significance tests. In the revised version, we will expand the Experiments section to include the number of independent runs (with random seeds), standard deviations across runs, 95% confidence intervals, and statistical significance tests (such as paired t-tests against baselines) for the reported gains. revision: yes
Referee: [Method] Method section (reward extraction procedure): no analysis is provided showing that the extracted task vectors improve coverage of the evaluation task space (e.g., via convex-hull volume, principal-component span, or distance to unseen w vectors) rather than merely reweighting toward behaviors already present in the data-collection policy; without this, the 20% gain does not yet establish superior zero-shot generalization.

Authors: We acknowledge that a direct analysis of task-space coverage would provide stronger support for the claim of improved generalization. The current manuscript relies on the empirical zero-shot performance gains as primary evidence. To address this, we will add to the revised manuscript a quantitative comparison of task vector distributions, including convex-hull volume of extracted versus randomly sampled vectors, principal component analysis of span, and average Euclidean distances to the unseen evaluation vectors w. This will help demonstrate that the extraction procedure improves coverage of the relevant task space. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical gains from dataset-derived sampling

full rationale

The paper presents an empirical method for extracting task vectors from the offline dataset to replace random sampling in zero-shot offline RL, reporting an average 20% improvement across benchmarks and baselines. No equations, derivations, or self-citations are shown that reduce the claimed performance gain to a fitted parameter, self-definition, or prior result by the same authors. The central claim rests on experimental comparison rather than a closed mathematical chain, and the extraction procedure is described as a simple integration into existing algorithms without invoking uniqueness theorems or ansatzes that collapse to inputs. The result is therefore self-contained as an observable empirical outcome.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on standard offline RL assumptions about dataset coverage and the validity of linear reward functions over state representations; no new free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption The offline dataset contains sufficient state-action coverage to extract representative task vectors for the broader task space.
Invoked when claiming the extracted vectors improve generalization to unseen rewards.

pith-pipeline@v0.9.0 · 5436 in / 1047 out tokens · 41509 ms · 2026-05-07T16:22:06.204764+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 23 canonical work pages · 4 internal anchors

[1]

8 Improving Zero-Shot Offline RL via Behavioral Task Sampling Borsa, D., Barreto, A., Quan, J., Mankowitz, D., Munos, R., Van Hasselt, H., Silver, D., and Schaul, T

URL https://doi.org/ 10.48550/arxiv.2402.01886. 8 Improving Zero-Shot Offline RL via Behavioral Task Sampling Borsa, D., Barreto, A., Quan, J., Mankowitz, D., Munos, R., Van Hasselt, H., Silver, D., and Schaul, T. Uni- versal successor features approximators.arXiv preprint arXiv:1812.07626,

work page doi:10.48550/arxiv.2402.01886
[2]

D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. Language models are few-shot learners. Advances in neural information processing systems, 33: 1877–1901,

1901
[3]

Exploration by random network distillation.arXiv preprint arXiv:1810.12894, 2018

Burda, Y ., Edwards, H., Storkey, A., and Klimov, O. Ex- ploration by random network distillation.arXiv preprint arXiv:1810.12894,

work page arXiv
[4]

B., Rockt¨aschel, T., and Grefenstette, E

Campero, A., Raileanu, R., K¨uttler, H., Tenenbaum, J. B., Rockt¨aschel, T., and Grefenstette, E. Learning with amigo: Adversarially motivated intrinsic goals.arXiv preprint arXiv:2006.12122,

work page arXiv 2006
[5]

Stein variational goal generation for adaptive exploration in multi-goal reinforcement learning.arXiv preprint arXiv:2206.06719,

Castanet, N., Lamprier, S., and Sigaud, O. Stein variational goal generation for adaptive exploration in multi-goal reinforcement learning.arXiv preprint arXiv:2206.06719,

work page arXiv
[6]

Unsupervised zero-shot reinforcement learning via functional reward encodings.arXiv preprint arXiv:2402.17135,

Frans, K., Park, S., Abbeel, P., and Levine, S. Unsupervised zero-shot reinforcement learning via functional reward encodings.arXiv preprint arXiv:2402.17135,

work page arXiv
[7]

and Ramponi, G

Freihaut, T. and Ramponi, G. On feasible rewards in multi- agent inverse reinforcement learning.arXiv preprint arXiv:2411.15046,

work page arXiv
[8]

and Ramponi, G

URL https://doi.org/ 10.48550/arxiv.2411.15046. Fu, J., Levine, S., and Abbeel, P. Learning robust rewards with adversarial inverse reinforcement learning. InInter- national Conference on Learning Representations (ICLR),

work page doi:10.48550/arxiv.2411.15046
[9]

Open-endedness is essential for artificial superhuman intelligence.arXiv preprint arXiv:2406.04268,

Hughes, E., Dennis, M., Parker-Holder, J., Behbahani, F., Mavalankar, A., Shi, Y ., Schaul, T., and Rocktaschel, T. Open-endedness is essential for artificial superhuman intelligence.arXiv preprint arXiv:2406.04268,

work page arXiv
[10]

Zero-shot rein- forcement learning via function encoders.arXiv preprint arXiv:2401.17173,

Ingebrand, T., Zhang, A., and Topcu, U. Zero-shot rein- forcement learning via function encoders.arXiv preprint arXiv:2401.17173,

work page arXiv
[11]

H-gap: Hu- manoid control with a generalist planner.arXiv preprint arXiv:2312.02682,

9 Improving Zero-Shot Offline RL via Behavioral Task Sampling Jiang, Z., Xu, Y ., Wagener, N., Luo, Y ., Janner, M., Grefen- stette, E., Rockt ¨aschel, T., and Tian, Y . H-gap: Hu- manoid control with a generalist planner.arXiv preprint arXiv:2312.02682,

work page arXiv
[12]

48550/arxiv.2501.12633

URL https://doi.org/10. 48550/arxiv.2501.12633. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y ., et al. Segment anything. InProceedings of the IEEE/CVF international conference on computer vision, pp. 4015–4026,

work page arXiv
[13]

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

URL https://arxiv.org/abs/2506.10137. Ledoux, M.The Concentration of Measure Phe- nomenon. Mathematical surveys and monographs. Amer- ican Mathematical Society,

work page internal anchor Pith review Pith/arXiv arXiv
[14]

Goal-conditioned re- inforcement learning: Problems and solutions.arXiv preprint arXiv:2201.08299,

ISBN 9780821837924. URL https://books.google.fr/books?id= mCX_cWL6rqwC. Liu, M., Zhu, M., and Zhang, W. Goal-conditioned re- inforcement learning: Problems and solutions.arXiv preprint arXiv:2201.08299,

work page arXiv
[15]

Playing Atari with Deep Reinforcement Learning

Mnih, V ., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. Playing atari with deep reinforcement learning.arXiv preprint arXiv:1312.5602,

work page internal anchor Pith review arXiv
[16]

arXiv preprint arXiv:1903.03698 , year=

Pong, V . H., Dalal, M., Lin, S., Nair, A., Bahl, S., and Levine, S. Skew-fit: State-covering self-supervised re- inforcement learning.arXiv preprint arXiv:1903.03698,

work page arXiv 1903
[17]

Automatic curriculum learning for deep rl: A short survey.arXiv preprint arXiv:2003.04664,

Portelas, R., Colas, C., Weng, L., Hofmann, K., and Oudeyer, P.-Y . Automatic curriculum learning for deep rl: A short survey.arXiv preprint arXiv:2003.04664,

work page arXiv 2003
[18]

Sigaud, O., Baldassarre, G., Colas, C., Doncieux, S., Duro, R., Oudeyer, P.-Y ., Perrin-Gilbert, N., and Santucci, V . G. A definition of open-ended learning problems for goal- conditioned agents.arXiv preprint arXiv:2311.00344,

work page arXiv
[19]

Tassa, Y ., Doron, Y ., Muldal, A., Erez, T., Li, Y ., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al. Deepmind control suite.arXiv preprint arXiv:1801.00690,

work page internal anchor Pith review arXiv
[20]

Does zero- shot reinforcement learning exist?arXiv preprint arXiv:2209.14935,

Touati, A., Rapin, J., and Ollivier, Y . Does zero- shot reinforcement learning exist?arXiv preprint arXiv:2209.14935,

work page arXiv
[21]

Finetuned Language Models Are Zero-Shot Learners

Wei, J., Bosma, M., Zhao, V . Y ., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V . Finetuned lan- guage models are zero-shot learners.arXiv preprint arXiv:2109.01652,

work page internal anchor Pith review arXiv
[22]

Deep maximum entropy inverse reinforcement learning.arXiv preprint arXiv:1507.04888,

Wulfmeier, M., Ondruska, P., and Posner, I. Deep maximum entropy inverse reinforcement learning.arXiv preprint arXiv:1507.04888,

work page arXiv
[23]

Deep maximum entropy inverse reinforcement learning.arXiv preprint arXiv:1507.04888,

URL https://doi.org/ 10.48550/arxiv.1507.04888. Yarats, D., Brandfonbrener, D., Liu, H., Laskin, M., Abbeel, P., Lazaric, A., and Pinto, L. Don’t change the algorithm, change the data: Exploratory data for offline reinforce- ment learning.arXiv preprint arXiv:2201.13425,

work page doi:10.48550/arxiv.1507.04888
[24]

Language to rewards for robotic skill synthesis

Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.-H., Are- nas, M. G., Chiang, H.-T. L., Erez, T., Hasenclever, L., Humplik, J., et al. Language to rewards for robotic skill synthesis.arXiv preprint arXiv:2306.08647,

work page arXiv
[25]

The critic uses twin Q-networks with analogous two-stream preprocessing of(s, a, z)tuples for stable value estimation

with task-conditioned networks: the actor π(s, z) uses parallel preprocessing streams (one for state features, another for task-conditioned states) with LayerNorm-Tanh initialization followed by ReLU activations, converging through a shared trunk network with Tanh output activation and truncated normal exploration (σ= 0.2 ). The critic uses twin Q-network...

2048
[26]

Each dataset consists of 107 total transitions, composed of 104 trajectories with a length of10 3 steps each

benchmark collected via Random Network Distillation (RND) (Burda et al., 2018). Each dataset consists of 107 total transitions, composed of 104 trajectories with a length of10 3 steps each. 14 Improving Zero-Shot Offline RL via Behavioral Task Sampling D. Baselines details This section provides objective functions for all baseline representation learning ...

2018
[27]

Here, f and ϕ are the learned factors that represent the state and its temporally extended features

learns a low-rank factorization of the successor measure M(s, s ′) using temporal difference learning. Here, f and ϕ are the learned factors that represent the state and its temporally extended features. min f,ϕ E(st,st+1)∼D s′∼D h f(s t)⊤ϕ(s′)−γf(s t+1)⊤ ¯ϕ(s′) 2i −2E (st,st+1)∼D f(s t)⊤ϕ(st+1) . • Bootstrap Your Own Latent (BYOL) (Grill et al., 2020)lea...

2020