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arxiv: 2606.03017 · v1 · pith:LNP6CGFUnew · submitted 2026-06-02 · 💻 cs.LG · cs.AI· cs.RO

ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL

Yikang Gui , Bikramjit Banerjee , Prashant Doshi This is my paper

Pith reviewed 2026-06-28 11:27 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.RO
keywords inverse reinforcement learningcontrastive learningtransferable IRLfactorized representationslatent abstractionscontinuous controlreward transferfew-shot transfer
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The pith

ConTraIRL decouples dynamics and goals into separate latents to enable reward transfer in IRL to unseen combinations.

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

The paper proposes a framework that learns separate latent spaces for environment dynamics and task goals so that rewards inferred from one pairing can be reused on new pairings. A dual-encoder network is trained with two contrastive losses: one aligns observations over time to make the dynamics encoder ignore goals, while the other makes the goal encoder ignore dynamics. Once trained, the factors can be recombined to infer rewards in novel settings without retraining from scratch. A sympathetic reader cares because standard IRL methods fail to generalize when both the physics and the objective change at once. Experiments on continuous control tasks show the approach improves few-shot transfer and reward accuracy over prior transfer baselines.

Core claim

ConTraIRL uses a dual-encoder architecture that maps observations into separate dynamics and goal latent spaces, trained with a dual contrastive objective. Temporal alignment encourages the dynamics encoder to learn goal-invariant structure, while the goal encoder captures dynamics-invariant features. This factorization supports reward inference under recombined dynamics-goal settings.

What carries the argument

Dual-encoder architecture trained with dual contrastive objective that enforces temporal alignment for dynamics invariance and dynamics invariance for goals.

If this is right

  • Few-shot transfer becomes possible to previously unseen dynamics-goal pairings.
  • Sample efficiency increases during reward recovery in the new settings.
  • Reward accuracy exceeds that of existing transfer IRL baselines on continuous control tasks.

Where Pith is reading between the lines

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

  • The same separation might help other RL transfer problems where multiple independent factors must be recombined.
  • If the latents remain disentangled at scale, the method could reduce retraining costs when only one factor changes in deployed systems.
  • Extending the encoders to handle partial observability or discrete actions would test whether the contrastive alignment generalizes beyond the continuous benchmarks used.

Load-bearing premise

The dual contrastive objective reliably produces decoupled latent representations of dynamics and goals that support accurate reward inference when the factors are recombined.

What would settle it

If reward recovery or sample efficiency on continuous control benchmarks does not improve for unseen dynamics-goal pairings relative to transfer IRL baselines, the factorization claim would fail.

Figures

Figures reproduced from arXiv: 2606.03017 by Bikramjit Banerjee, Prashant Doshi, Yikang Gui.

Figure 1
Figure 1. Figure 1: Illustration of dynamics–goal factorization. When [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: ConTraIRL overview. ConTraIRL trains jointly on source and target environments with few-shot expert states. A dynamics encoder and a goal encoder encode states into factor-specific latent abstractions. Expert structure learning shapes the expert manifolds, while expert–learner contrastive calibration separates learner representations from expert behavior. The reward is computed by measuring similarity betw… view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of the Context-Modulated Encoder. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Learning curves in target contexts. The ground [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of noise in factor labels. Normalized return [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: UMAP visualization of the learned dynamics and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Additional UMAP visualization of the learned abstractions. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: presents additional learning curves across environments and contextual splits. In all cases, the return computed under the learned reward closely tracks the return under the ground-truth environment reward throughout training. The reported Pearson correlation coefficients (r) quantify this alignment and remain consistently high across settings. This behavior indicates that the recovered reward provides a s… view at source ↗
read the original abstract

Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factorized Contrastive Abstractions for Transferable IRL (ConTraIRL), a framework that enables compositional reward transfer by learning decoupled latent representations of these two factors. ConTraIRL uses a dual-encoder architecture that maps observations into separate dynamics and goal latent spaces, trained with a dual contrastive objective. Temporal alignment encourages the dynamics encoder to learn goal-invariant structure, while the goal encoder captures dynamics-invariant features. This factorization supports reward inference under recombined dynamics-goal settings. Experiments on continuous control benchmarks demonstrate effective few-shot transfer to unseen dynamics-goal pairings, improving sample efficiency and reward recovery over transfer IRL baselines.

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 / 0 minor

Summary. The manuscript proposes ConTraIRL, a framework for transferable inverse reinforcement learning that learns decoupled latent representations of environment dynamics and task goals. It employs a dual-encoder architecture trained via a dual contrastive objective, with temporal alignment used to promote goal-invariant dynamics features and dynamics-invariant goal features. This factorization is intended to support reward inference and policy transfer under recombined, unseen dynamics-goal pairings. Experiments on continuous control benchmarks are reported to demonstrate improved few-shot transfer, sample efficiency, and reward recovery relative to transfer IRL baselines.

Significance. If the factorization claim holds with reliable decoupling, the work would address a key limitation in IRL transfer by enabling compositional generalization without retraining on every dynamics-goal combination. The approach is notable for attempting to achieve this via contrastive objectives rather than explicit regularizers, though the strength of the result depends on empirical validation of the latent independence.

major comments (2)
  1. [Abstract] Abstract: the central claim that the dual contrastive objective with temporal alignment produces dynamics latents that are goal-invariant and goal latents that are dynamics-invariant is load-bearing for the recombination results, yet the abstract provides no explicit independence regularizer, cycle-consistency term, or mutual-information penalty that would guarantee statistical decoupling when dynamics and goals are correlated in the training distribution.
  2. [Abstract] Abstract: without reported measures (e.g., mutual information estimates or ablation on latent recombination accuracy) it is unclear whether residual correlations remain, which would directly undermine the few-shot transfer improvements claimed for unseen pairings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting the need for clearer justification of the factorization mechanism in the abstract. We address each comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the dual contrastive objective with temporal alignment produces dynamics latents that are goal-invariant and goal latents that are dynamics-invariant is load-bearing for the recombination results, yet the abstract provides no explicit independence regularizer, cycle-consistency term, or mutual-information penalty that would guarantee statistical decoupling when dynamics and goals are correlated in the training distribution.

    Authors: The dual contrastive objective, combined with temporal alignment for positive/negative pair construction, is the mechanism intended to encourage goal-invariant dynamics features and dynamics-invariant goal features. No additional explicit regularizer is used because the contrastive losses directly optimize for the desired separation via the sampling strategy. We will revise the abstract to more explicitly describe how the dual contrastive losses achieve this without relying on supplementary penalties. revision: yes

  2. Referee: [Abstract] Abstract: without reported measures (e.g., mutual information estimates or ablation on latent recombination accuracy) it is unclear whether residual correlations remain, which would directly undermine the few-shot transfer improvements claimed for unseen pairings.

    Authors: The current experiments focus on downstream transfer performance, but we agree that direct quantification of latent independence would strengthen the claims. We will add mutual information estimates between the two latent spaces and an ablation measuring recombination accuracy under controlled correlation levels in the revised version. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper proposes a new dual-encoder architecture and dual contrastive objective (with temporal alignment) to learn factorized dynamics and goal latents for IRL transfer. No derivation chain is presented that reduces a claimed prediction or first-principles result to its own inputs by construction, nor are there load-bearing self-citations, fitted inputs renamed as predictions, or ansatzes smuggled via prior work. The method is defined explicitly by its components, and claims rest on empirical evaluation rather than self-referential fitting or renaming of known results, rendering the approach self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no information on free parameters, background axioms, or new entities.

pith-pipeline@v0.9.1-grok · 5664 in / 920 out tokens · 24153 ms · 2026-06-28T11:27:52.401681+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

19 extracted references · 1 canonical work pages · 1 internal anchor

  1. [1]

    Environment design for inverse reinforcement learning

    Thomas Kleine Buening, Victor Villin, and Christos Dimitrakakis. Environment design for inverse reinforcement learning. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024 , volume 235 of Proceedings of Machine Learning Research, pages 24808--24828. PMLR / OpenReview.net, 2024. URL https://proceedings....

    2024

  2. [2]

    Multi-task hierarchical adversarial inverse reinforcement learning

    Jiayu Chen, Dipesh Tamboli, Tian Lan, and Vaneet Aggarwal. Multi-task hierarchical adversarial inverse reinforcement learning. In International Conference on Machine Learning, pages 4895--4920. PMLR, 2023

    2023

  3. [3]

    A simple framework for contrastive learning of visual representations

    Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597--1607. PmLR, 2020

    2020

  4. [4]

    Learning robust rewards with adversarial inverse reinforcement learning

    Justin Fu, Katie Luo, and Sergey Levine. Learning robust rewards with adversarial inverse reinforcement learning. arXiv preprint arXiv:1710.11248, 2017

    Pith/arXiv arXiv 2017

  5. [5]

    State-only imitation with transition dynamics mismatch

    Tanmay Gangwani and Jian Peng. State-only imitation with transition dynamics mismatch. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020. URL https://openreview.net/forum?id=HJgLLyrYwB

    2020

  6. [6]

    Iq-learn: Inverse soft-q learning for imitation

    Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, and Stefano Ermon. Iq-learn: Inverse soft-q learning for imitation. Advances in Neural Information Processing Systems, 34: 0 4028--4039, 2021

    2021

  7. [7]

    Inversely Learning Transferable Rewards via Abstracted States

    Yikang Gui and Prashant Doshi. Inversely learning transferable rewards via abstracted states. CoRR, abs/2501.01669, 2025. doi:10.48550/ARXIV.2501.01669. URL https://doi.org/10.48550/arXiv.2501.01669

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2501.01669 2025

  8. [8]

    Dai, and Quoc V

    David Ha, Andrew M. Dai, and Quoc V. Le. Hypernetworks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net, 2017. URL https://openreview.net/forum?id=rkpACe1lx

    2017

  9. [9]

    Generative adversarial imitation learning

    Jonathan Ho and Stefano Ermon. Generative adversarial imitation learning. Advances in neural information processing systems, 29, 2016

    2016

  10. [10]

    Non-adversarial inverse reinforcement learning via successor feature matching

    Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, and Sanjiban Choudhury. Non-adversarial inverse reinforcement learning via successor feature matching. arXiv preprint arXiv:2411.07007, 2024

    arXiv 2024

  11. [11]

    Supervised contrastive learning

    Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33: 0 18661--18673, 2020

    2020

  12. [12]

    Adversarial self-supervised contrastive learning

    Minseon Kim, Jihoon Tack, and Sung Ju Hwang. Adversarial self-supervised contrastive learning. Advances in neural information processing systems, 33: 0 2983--2994, 2020

    2020

  13. [13]

    Tw-crl: Time-weighted contrastive reward learning for efficient inverse reinforcement learning

    Yuxuan Li, Yicheng Gao, Ning Yang, and Stephen Xia. Tw-crl: Time-weighted contrastive reward learning for efficient inverse reinforcement learning. arXiv preprint arXiv:2504.05585, 2025

    arXiv 2025

  14. [14]

    Umap: Uniform manifold approximation and projection for dimension reduction

    Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018

    Pith/arXiv arXiv 2018

  15. [15]

    Comi-irl: Contrastive multi-intention inverse reinforcement learning

    Antonio Mone, Frans A Oliehoek, and Luciano Cavalcante Siebert. Comi-irl: Contrastive multi-intention inverse reinforcement learning. arXiv preprint arXiv:2602.07496, 2026

    arXiv 2026

  16. [16]

    Multi-modal inverse constrained reinforcement learning from a mixture of demonstrations

    Guanren Qiao, Guiliang Liu, Pascal Poupart, and Zhiqiang Xu. Multi-modal inverse constrained reinforcement learning from a mixture of demonstrations. Advances in Neural Information Processing Systems, 36: 0 60384--60396, 2023

    2023

  17. [17]

    Dec-airl: Decentralized adversarial irl for human-robot teaming

    Prasanth Sengadu Suresh, Yikang Gui, and Prashant Doshi. Dec-airl: Decentralized adversarial irl for human-robot teaming. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pages 1116--1124, 2023

    2023

  18. [18]

    What makes for good views for contrastive learning? Advances in neural information processing systems, 33: 0 6827--6839, 2020

    Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? Advances in neural information processing systems, 33: 0 6827--6839, 2020

    2020

  19. [19]

    Meta-inverse reinforcement learning with probabilistic context variables

    Lantao Yu, Tianhe Yu, Chelsea Finn, and Stefano Ermon. Meta-inverse reinforcement learning with probabilistic context variables. Advances in neural information processing systems, 32, 2019

    2019