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arxiv: 2509.26627 · v3 · pith:QV3YXPL6new · submitted 2025-09-30 · 💻 cs.AI · cs.LG· cs.RO

TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance

Yuyang Liu , Chuan Wen , Yihang Hu , Dinesh Jayaraman , Yang Gao This is my paper

Pith reviewed 2026-05-21 21:53 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.RO
keywords dense reward learningreinforcement learningpassive videostemporal distanceroboticsMeta-Worldreward shaping
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The pith

TimeRewarder learns dense rewards for RL by estimating temporal distances between frames in passive videos.

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

The paper seeks to replace manual dense reward design in robotics reinforcement learning with a scalable alternative drawn from passive videos. It treats the temporal separation between video frames as a direct measure of task progress that can serve as a step-wise reward signal. By training a model to predict these distances, the method generates proxy rewards that guide policy learning without task-specific engineering. Experiments across ten Meta-World tasks show the resulting rewards enable near-perfect success rates in nine tasks after only 200,000 interactions, surpassing both prior learning methods and hand-crafted dense rewards. The same pretraining pipeline also accepts real-world human videos, pointing toward broader use of everyday footage for reward signals.

Core claim

TimeRewarder derives progress estimation signals from passive videos, including robot demonstrations and human videos, by modeling temporal distances between frame pairs and supplies these signals as step-wise proxy rewards to guide reinforcement learning in sparse-reward robotics tasks.

What carries the argument

A frame-pair temporal distance predictor trained on passive videos that converts predicted distances into dense progress-based rewards for RL.

If this is right

  • RL agents reach near-perfect success in nine of ten Meta-World tasks using only 200,000 environment interactions each.
  • The learned rewards improve both final performance and sample efficiency over previous reward-learning methods and manually designed dense rewards.
  • The same approach can be pretrained on real-world human videos, enabling reward signals from diverse non-robot sources.
  • No task-specific reward engineering is required once the temporal-distance model is trained.

Where Pith is reading between the lines

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

  • The method may extend to other sequential decision domains where video or trajectory data is abundant but dense rewards are scarce.
  • If temporal distance proves robust across viewpoints, similar models could support reward learning from internet-scale video without robot-specific data collection.
  • Combining the temporal-distance signal with other unsupervised objectives from the same videos could produce even richer reward functions.
  • The approach invites testing whether the learned rewards transfer zero-shot to new robot embodiments or tasks not seen during video pretraining.

Load-bearing premise

Temporal distances between frames in passive videos reliably indicate task progress that remains useful when transferred as rewards to a robot's own observations.

What would settle it

An RL policy trained with TimeRewarder rewards on a Meta-World task achieves success rates no higher than a sparse-reward baseline after 200,000 interactions.

Figures

Figures reproduced from arXiv: 2509.26627 by Chuan Wen, Dinesh Jayaraman, Yang Gao, Yihang Hu, Yuyang Liu.

Figure 1
Figure 1. Figure 1: Overview of TimeRewarder. Mirroring how humans infer task progression by observing others, TimeRewarder distills frame-wise temporal distances from expert videos and converts them into dense reward signals, thereby enabling reinforcement learning free of manually engineered rewards or action annotations. Reinforcement learning (RL) has long served as a principal paradigm for robotic skill acquisi￾tion (Iba… view at source ↗
Figure 2
Figure 2. Figure 2: TimeRewarder framework. TimeRewarder learns step-wise dense rewards from passive videos by modeling intrinsic temporal distances, enabling robust progress scoring that assigns high values to states reflecting task advancement, while penalizing suboptimal actions lacking meaningful contribution to task progression, thereby facilitating effective policy learning. We introduce TimeRewarder, a framework that d… view at source ↗
Figure 3
Figure 3. Figure 3: Value–Order Correlation (VOC) on held-out expert videos. Higher is better. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reward/value curves on successful (traj1) vs. failed (traj2) rollouts for two tasks. TimeRewarder and VIP output values (cumulative progress), PROGRESSOR outputs step￾wise rewards, while Rank2Reward is visualized through its pairwise ordering reward signals. TimeRewarder provides the most informative and temporally coherent feedback. its few-shot setting by giving 5 expert videos as context and another 5 f… view at source ↗
Figure 5
Figure 5. Figure 5: Performance of reinforcement learning with sparse environment success signals and dense [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cross-domain reward learning. TimeRewarder improves performance by leveraging 20 unlabeled human videos alongside only 1 in-domain Meta-World demonstration per task, demon￾strating its ability to utilize cross-domain visual data. Curves show mean ± s.d. over eight seeds. consistent distinctions. These comparative results demonstrate TimeRewarder’s unique capacity for temporally coherent and causally ground… view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study results. Curves show mean [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Meta-World tasks used in our paper. A.2 HUMAN VIDEO DATASETS FOR CROSS-DOMAIN EXPERIMENTS This section presents the complete set of human videos used in the cross-domain experiments across three tasks. Each task includes 20 videos recorded in single-view (fixed viewpoint) and 20 videos recorded in multi-view (varying viewpoints) conditions. These videos differ from the robot setting in embodiment and backg… view at source ↗
Figure 9
Figure 9. Figure 9: Complete set of human videos recorded in the [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Complete set of human videos recorded in the [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: VIP performance with ResNet34 vs. ViT backbones across tasks. The results show that [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Reinforcement learning without sparse reward. Curves show mean [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
read the original abstract

Designing dense rewards is crucial for reinforcement learning (RL), yet in robotics it often demands extensive manual effort and lacks scalability. One promising solution is to view task progress as a dense reward signal, as it quantifies the degree to which actions advance the system toward task completion over time. We present TimeRewarder, a simple yet effective reward learning method that derives progress estimation signals from passive videos, including robot demonstrations and human videos, by modeling temporal distances between frame pairs. We then demonstrate how TimeRewarder can supply step-wise proxy rewards to guide reinforcement learning. In our comprehensive experiments on ten challenging Meta-World tasks, we show that TimeRewarder dramatically improves RL for sparse-reward tasks, achieving nearly perfect success in 9/10 tasks with only 200,000 environment interactions per task. This approach outperformed previous methods and even the manually designed environment dense reward on both the final success rate and sample efficiency. Moreover, we show that TimeRewarder pretraining can exploit real-world human videos, highlighting its potential as a scalable approach to rich reward signals from diverse video sources.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces TimeRewarder, a method to derive dense rewards for RL by training a model on passive videos (robot demos or human videos) to predict temporal distances between frame pairs, then using the resulting progress signal as step-wise proxy rewards. It reports that this yields near-perfect success on 9/10 Meta-World tasks using only 200k environment steps per task, outperforming prior methods and even the hand-designed environment dense reward, while also showing applicability to real-world human videos.

Significance. If the central empirical claims prove robust, the work offers a scalable route to progress-based dense rewards from abundant video data, reducing manual reward engineering in robotics RL. The reported outperformance on multiple sparse-reward tasks and the extension to human videos would constitute a practical contribution to sample-efficient policy learning.

major comments (2)
  1. [§4 Experiments] §4 Experiments: The headline results on ten Meta-World tasks report high success rates and sample efficiency but provide neither error bars, the number of random seeds, nor ablation tables on video source selection and hyperparameter sensitivity; without these it is impossible to assess whether the claimed gains over baselines and the environment dense reward are statistically reliable or brittle.
  2. [§3 Method] §3 Method: The derivation of the reward from the learned temporal-distance model does not include explicit handling or empirical tests for distribution shift (viewpoint, background, gripper appearance, or motion statistics) between the passive training videos and the states visited during RL exploration; this assumption is load-bearing for the claim that the scalar reward gradients point toward task completion.
minor comments (2)
  1. [§3] Notation for the temporal distance function and its conversion to a per-step reward could be stated more explicitly in the equations to improve reproducibility.
  2. [Figures] Figure captions and legends should clarify which video sources (robot demos vs. human videos) correspond to each curve.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on experimental reporting and the treatment of distribution shift. We address each major comment below and indicate the revisions planned for the next manuscript version.

read point-by-point responses

  1. Referee: [§4 Experiments] §4 Experiments: The headline results on ten Meta-World tasks report high success rates and sample efficiency but provide neither error bars, the number of random seeds, nor ablation tables on video source selection and hyperparameter sensitivity; without these it is impossible to assess whether the claimed gains over baselines and the environment dense reward are statistically reliable or brittle.

    Authors: We agree that explicit reporting of error bars, the number of random seeds, and additional ablations would strengthen the assessment of statistical reliability. The experiments were run with 5 random seeds per task, with the headline numbers reflecting averages; we will add error bars to the main results table and figures in the revised manuscript. We will also include ablation tables on video source selection (robot demonstrations versus human videos) and hyperparameter sensitivity in the supplementary material to demonstrate that the reported gains are not brittle. revision: yes

  2. Referee: [§3 Method] §3 Method: The derivation of the reward from the learned temporal-distance model does not include explicit handling or empirical tests for distribution shift (viewpoint, background, gripper appearance, or motion statistics) between the passive training videos and the states visited during RL exploration; this assumption is load-bearing for the claim that the scalar reward gradients point toward task completion.

    Authors: The referee correctly notes that the method relies on the temporal-distance model transferring from passive videos to RL states without dedicated shift-handling mechanisms. While the successful transfer to real-world human videos provides some empirical support for robustness under natural shifts in viewpoint and appearance, we did not include explicit tests or mitigation strategies in the original submission. In the revision we will add a discussion paragraph in Section 3 on this assumption together with a small-scale experiment evaluating reward prediction accuracy under controlled distribution shifts (e.g., background and gripper changes). revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper trains a supervised temporal-distance model on frame pairs drawn from passive videos, using the known elapsed time between frames as the regression target. This model is then applied to produce scalar proxy rewards inside an independent RL loop on Meta-World tasks. The central claims are supported by external benchmark comparisons (success rates and sample efficiency against prior methods and hand-designed dense rewards) rather than any redefinition of fitted quantities as predictions or any load-bearing self-citation chain. No equation reduces the output reward to the training inputs by construction, and the approach remains falsifiable on held-out robot trajectories.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that temporal distance in video frames is a faithful proxy for task progress. No explicit free parameters or invented entities are described in the abstract; the method appears to rely on standard supervised learning of a distance predictor.

axioms (1)
  • domain assumption Temporal distance between frames correlates with task progress in a way that is transferable to RL reward signals.
    This premise is invoked when the learned distance model is used to supply step-wise rewards during RL training.

pith-pipeline@v0.9.0 · 5734 in / 1339 out tokens · 27189 ms · 2026-05-21T21:53:09.513102+00:00 · methodology

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Lean theorems connected to this paper

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    Relation between the paper passage and the cited Recognition theorem.

    We present TimeRewarder, a simple yet effective reward learning method that derives progress estimation signals from passive videos... by modeling temporal distances between frame pairs... rTR(ot, ot+1) = Φ^{-1}[Fθ(ot, ot+1)] ∈ [-1,1]

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Reference graph

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