Return-Guided Contrastive Learning

• Return-guided contrastive learning is a method that fuses long-term reward signals with contrastive pair construction to align representations with optimal outcomes.
• It uses return-based sampling, soft assignments, and ranking to effectively segment trajectory data, enhancing latent space structuring for diverse applications.
• Empirical results show that integrating return signals improves sample efficiency, representation transfer, and robustness across RL, planning, and graph learning benchmarks.

Return-guided contrastive learning comprises a family of methods in representation learning and reinforcement learning (RL) that integrate the outcome-driven signal—typically, long-term return or reward—into the construction or scoring of contrastive pairs. By leveraging return-based supervision rather than purely unsupervised signals, these approaches seek to preferentially cluster states, state–action pairs, or representations that are aligned with higher expected outcomes and to distinguish those linked to lower returns. This methodology has gained traction across deep RL, diffusion-guided planning, graph learning, vision RL, and beyond.

1. Theoretical Foundations and Core Principles

Return-guided contrastive learning operates at the intersection of representation learning and outcome-based supervision. The core theoretical construct is to bias the contrastive learning process such that latent representations are organized by their associated return or value signal—directly incorporating task-critical feedback into unsupervised or auxiliary objectives.

In canonical return-based contrastive learning for RL (Liu et al., 2021), trajectory data is segmented by events tied to returns (e.g., reward events, cumulative thresholds), and the contrastive loss is formulated with positive pairs sampled from within the same return-defined segment and negatives sampled across segments. If $f(x)$ denotes the embedding of a state–action pair $x = (s, a)$, the key auxiliary objective can be expressed as: $$\mathcal{L}_{\text{aux}} = \mathbb{E}_{(x, x^+, x^-)} [\ell(f(x), f(x^+)) - \ell(f(x), f(x^-))]$$ where $x^+$ (positive) shares similar long-horizon return distribution with $x$, and $x^-$ (negative) is differently segmented. In diffusion-based planners (e.g., CDiffuser (2402.02772)), a contrastive loss is used to bring generated states closer in latent space to high-return dataset states and repel them from low-return samples, based on soft return-driven grouping.

This approach is theoretically justified by its effect on the structure of the learned representation space: it induces a state (or state-action) abstraction where equivalence is determined by similarity of return distributions under the current or target policy. Analytical results demonstrate that maximizing the contrastive objective under this partitioning yields representations aligned with optimal control or planning, particularly benefiting sample efficiency and generalization in low-data regimes (Liu et al., 2021).

2. Return-Guided Sampling and Pair Construction

A defining feature of return-guided contrastive frameworks is the explicit or probabilistic use of return information for constructing positive and negative pairs.

• Segmentation: In RL settings, trajectories are segmented by return events (nonzero reward, cumulative thresholds). Positive pairs are drawn from within segments, negatives from outside (Liu et al., 2021).
• Soft Assignment: Rather than hard clustering, CDiffuser assigns soft probabilities to each sample via sigmoidal functions of their returns: high-return states receive higher $p^+$, low-return states higher $p^-$, with the sharpness and boundary controlled by tunable parameters. This allows for nuanced supervision near the return boundaries (2402.02772).
• Ranking and Prior Information: In graph learning, methods like coarse-to-fine contrastive learning (Zhao et al., 2022) utilize the degree of data augmentation (which correlates with similarity to the original structure) to establish an ordered “ranking” among views. Positive pairs are not only distinguished from negatives, but are ranked according to their “return” (augmentation strength as a surrogate for fidelity).
• Manifold-based Selection: Self-reinforced graph contrastive learning (Hsieh et al., 19 May 2025) selects high-quality positive pairs based on encoder output distances, with probabilistic weighting decaying over epochs (temperature decay), resulting in a self-reinforcing feedback reminiscent of a return-guided loop.

This targeted sampling improves the quality of positive samples, aligns supervision with performance-relevant structure, and addresses issues such as false positives in graph augmentations or distractor features in vision RL (Lee et al., 9 Oct 2025).

3. Integration into RL and Planning

Return-guided contrastive learning has been deployed as an auxiliary task or core module in RL and offline planning algorithms.

• Auxiliary Loss in RL: When added to a standard RL pipeline (e.g., Rainbow for Atari (Liu et al., 2021)), the return-based contrastive loss is optimized jointly with the primary RL objective. This shapes the encoder’s latent space to reflect return-based equivalence, empirically yielding improved sample efficiency, especially in low-data settings.
• Diffusion Model Guidance: In approaches like CDiffuser, the contrastive loss operates in the denoising loop of a diffusion-based trajectory generator, directly reshaping the generative base distribution to focus on high-return regions. The denoising process is modulated by gradients from a return predictor, further aligning generation with outcome-driven supervision (2402.02772).
• Visual RL and Attention: In Gaze on the Prize (Lee et al., 9 Oct 2025), a return-guided triplet loss acts on the outputs of a gaze (attention) mechanism over visual features. Anchor, positive, and negative samples are grouped based on return differences among similar representations, teaching the attention module to localize task-relevant visual cues and ignore distractions.

The modularity of return-guided losses allows integration with a wide range of base learners, including both model-free and diffusion-based planners, without modification of the underlying control policies.

4. Empirical Results and Impact

Comprehensive empirical studies consistently report that return-guided contrastive learning enhances downstream performance, representation quality, and sample efficiency across multiple domains.

RL and Planning:

• On D4RL locomotion and navigation benchmarks, CDiffuser achieves the top performance or matches the strongest existing planners, with particularly prominent gains on datasets with many low-return trajectories (2402.02772).
• Atari and DeepMind Control experiments with return-based auxiliary tasks report increased sample efficiency in low-data regimes compared to unsupervised auxiliary tasks (e.g., CURL, predictive coding) and match “skyline” models with privileged information (Liu et al., 2021).
• In vision RL, return-guided contrastive attention (Gaze on the Prize) improves sample efficiency by up to 2.4× and enables success on tasks that baseline agents fail, especially in the presence of visual distractors (Lee et al., 9 Oct 2025).

Graph Learning:

• Coarse-to-fine and self-reinforced graph contrastive methods outperform baseline GCL and supervised GNNs by leveraging prior- or encoder-driven structures to select and rank positives (Zhao et al., 2022, Hsieh et al., 19 May 2025).

Representation Transfer:

• When return-guided features are frozen and repurposed (e.g., via linear evaluation on STL-10), transferability and robustness are enhanced compared to vanilla contrastive or distillation-based learning (Bai et al., 2020).

5. Analysis, Interpretability, and Broader Connections

Return-guided contrastive learning is closely intertwined with contrastive objectives’ information-theoretic underpinnings, explainability, and robust clustering properties.

• Mutual Information Maximization: Lower bounds on $MI(t; s)$ (mutual information between anchor and student features) are derived directly via noise contrastive estimation, linking the return-guided objectives to classical information-theoretic goals (Bai et al., 2020).
• Ranking as Return Guidance: Theoretical reformulation of contrastive learning as a learning-to-rank problem enables fine-grained or listwise supervision, exploiting augmentation-order or explicit return as the guiding “return” (Zhao et al., 2022).
• Visualization and Explainability: Visual attention models trained with return-guided contrastive signals provide spatial maps indicating attended regions correlated with outcome differences, supporting interpretability and diagnostic analysis (Lee et al., 9 Oct 2025).
• Self-Reinforcement Loops: In graph domains, the probabilistic selection of positives leverages feedback from the encoder’s current representation quality—creating a virtuous cycle akin to outcome-guided learning in RL (Hsieh et al., 19 May 2025).

A plausible implication is that these mechanisms align the learned representations with performance-critical variations in the data, thereby mitigating shortcut learning (e.g., attention to distractors), reducing false positives/negatives in augmentations, and supporting generalizable feature discovery.

6. Limitations and Future Directions

Return-guided contrastive learning depends on the presence and variability of return signals. In environments with sparse, uninformative, or highly delayed rewards, segmentation and positive/negative supervision may degenerate. Several works suggest combining return-guided signals with alternative auxiliary rewards, exploring finer temporal dependencies (e.g., multi-step or trajectory-level abstractions), and developing adaptive grouping strategies (Lee et al., 9 Oct 2025, Liu et al., 2021, 2402.02772). Extensions to model-based RL, alternative control problems, and further refinement of attention mechanisms (beyond simple Gaussian parameterizations) remain open directions of high interest.

Domain/Problem	Return-Guided Supervision	Impact
RL state-action	Based on trajectory/segment return	Improved sample efficiency, task performance
Diffusion planning	Soft assignment by return	Higher prevalence of high-return trajectory generation
Visual RL	Triplets from return differences	Enhanced attention, sample efficiency, robustness
Graph learning	Ranking/selection by augmentation	Improved node classification, representation quality

7. Representative Algorithms and Key Mathematical Formulations

Representative return-guided contrastive loss functions include:

• Triplet Loss (Vision RL):

$$\mathcal{L}_{\text{con}}(\theta) = \mathbb{E}_{(o_a, o_+, o_-)} \big[\max(0, D(z_a, z_+) - D(z_a, z_-) + \alpha)\big]$$

with $D$ as one minus the cosine similarity.

• Softmax-Based Contrast (Diffusion RL):

$$\mathcal{L}^i_h = -\log \left[ \frac{\sum_k \exp(\text{sim}(f(\hat{s}^i_h), f(s^+_h))/T)}{\sum_k \exp(\text{sim}(f(\hat{s}^i_h), f(s^-_h))/T)} \right]$$

where positive/negative sets are defined via return-based soft assignments (2402.02772).

• Balanced Sampling (Canonical RL):

$$\mathcal{L}_{\text{aux}} = \mathbb{E}_{(x, x^+, x^-)} [\ell(f(x), f(x^+)) - \ell(f(x), f(x^-))]$$

with stratified sampling by return-derived segments (Liu et al., 2021).

• Listwise Ranking for Graphs:

$$\mathcal{L}_{\text{CF}} = \alpha\, \mathcal{L}_{\text{coarse}} + (1-\alpha)\,\mathcal{L}_{\text{fine}}$$

combining coarse ordering (by augmentation return) and fine-grained encoder-based supervision (Zhao et al., 2022).

• (Bai et al., 2020) Feature Distillation With Guided Adversarial Contrastive Learning
• (Liu et al., 2021) Return-Based Contrastive Representation Learning for Reinforcement Learning
• (Zhao et al., 2022) Coarse-to-Fine Contrastive Learning on Graphs
• (2402.02772) Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning
• (Hsieh et al., 19 May 2025) Self-Reinforced Graph Contrastive Learning
• (Lee et al., 9 Oct 2025) Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning

Return-guided contrastive learning unifies the strengths of contrastive representation learning and outcome-based RL supervision, producing representations and policies with improved robustness, efficiency, and interpretability across diverse domains.