Zero-Shot Reward Models

Updated 26 November 2025

Zero-shot reward models are techniques that compute rewards from universal pretrained encoders, eliminating the need for task-specific reward engineering.
They leverage vision-language models, large language models, and operator-based methods to generate immediate, scalable reward signals across diverse domains.
Experimental validations indicate these models achieve near-oracle RL performance and robust cross-domain adaptation despite challenges like domain shifts and computational overhead.

A zero-shot reward model quantifies agent behavior or outcome quality without any environment-specific training or ground-truth labels. Instead, it leverages pretrained models (vision-language, text-based, or multimodal), theoretical embeddings, or cross-domain preference transfer to instantly produce reward signals in new tasks or domains. This approach enables reinforcement learning (RL) agents to generalize or adapt to unseen objectives, user profiles, reward settings, or language instructions in a sample-efficient and scalable manner. Zero-shot reward models encompass vision-language alignment, LLM scoring, cross-task preference transfer, operator-theoretic RL, distributional successor features, and robust feedback correction.

1. Foundational Principles

Zero-shot reward models are rooted in the paradigm shift from fixed, environment-centric RL agents toward “controllable” agents that can follow arbitrary instructions, preferences, or descriptions (Touati et al., 2022). The essential principle is to replace manual reward engineering or labor-intensive supervised reward modeling with inference-time reward computation via generic pretrained models or environment embeddings. Zero-shot models are required to (1) assign reward using only generic or transferable criteria (e.g., CLIP similarity, MPNet profile alignment, LLM parses, Gromov–Wasserstein label transport), (2) avoid any per-task, per-profile, or per-environment fine-tuning, and (3) support immediate policy optimization or evaluation in new downstream settings.

Mathematically, such a reward model $R_{\mathrm{ZS}}$ is a deterministic or probabilistic mapping from an agent's observations and task specification (e.g., image $o$ and text prompt $l$ ; trajectory and preference; dialog context and user profile) to a scalar reward, computed via a fixed pretrained model or embedding (Rocamonde et al., 2023, Ollivier, 15 Feb 2025, Zhao et al., 2023). This formulation supports a broad family of RL tasks, including vision-language alignment, robotic manipulation, dialog personalization, continuous control, and cross-lingual transfer.

2. Vision-Language and Multimodal Zero-Shot Rewards

Pretrained vision-LLMs (VLMs) such as CLIP, Flamingo, and S3D are key enablers for zero-shot reward modeling in visually grounded RL (Rocamonde et al., 2023, Ye et al., 27 Mar 2024, Zhao et al., 2023). In VLM-based reward inference, an agent’s observation (image or video) and a natural-language prompt are mapped into a joint embedding space via pretrained encoders. The reward is computed as the cosine similarity or distance between the observation and the prompt embedding: $r_{\mathrm{CLIP}}(o, l) = \frac{\mathrm{CLIP}_L(l) \cdot \mathrm{CLIP}_I(o)}{\|\mathrm{CLIP}_L(l)\| \|\mathrm{CLIP}_I(o)\|},$ where $o$ is a rendered image and $l$ is the textual task description.

Specialized reward shaping arises in the choice of prompt. LORD (Ye et al., 27 Mar 2024) demonstrates that “opposite reward design”—using concrete descriptions of undesired outcomes (e.g., collision)—produces robust, generalizable rewards across traffic scenarios, whereas vague or abstract desired goals (e.g., “drive safely”) fail to ground reliably in the model's latent space. RLCF (Zhao et al., 2023) introduces reinforcement learning with CLIP feedback, using CLIP as a frozen critic for test-time adaptation in classification, retrieval, or captioning tasks. In these algorithms, zero-shot performance is consistently enhanced by optimizing the agent's parameters to maximize the reward assigned by the VLM, often using policy-gradient steps such as REINFORCE, combined with baseline subtraction for variance reduction.

3. LLM–Driven Reward Functions

LLMs such as GPT-3/4 and instruction-tuned variants (Flan-T5, PaLM-2) serve as powerful zero-shot reward models for intra-linguistic and cross-domain RL tasks (Kwon et al., 2023, Gallego, 2023, Nazir et al., 26 Mar 2025, Siddique et al., 2023). In LLM-based frameworks, natural-language instructions, objective definitions, or user preferences are encoded directly into the reward via structured prompts. For instance, reward queries can take the form:

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Text: {output_text}
Question: {binary yes/no question}
Response:

The probability that the LLM answers “Yes” is used as the reward (Gallego, 2023). For matrix games, social dilemmas, or negotiations, structured prompt templates define the game, objectives, and ask for a binary or ranked judgment (Kwon et al., 2023). The reward model parses the LLM output via post-processing (e.g., mapping “Yes” to 1 and “No” to 0).

Computation of LLM-derived rewards, in zero-shot settings, depends on careful prompt engineering and, sometimes, chain-of-thought strategies. In dialog personalization, reward functions are constructed to simultaneously measure semantic alignment to user profiles and penalize task deviation through KL divergence (Siddique et al., 2023). Recent work extends LLM reward modeling to continuous control, employing prompt-based bias correction and feedback hybridization for robust reward shaping in human-in-the-loop RL (Nazir et al., 26 Mar 2025).

4. Preference Transfer, Cross-Domain and Cross-Lingual Alignment

Zero-shot reward modeling extends beyond monolithic pretrained models to the transfer of preferences and reward models across tasks, domains, and languages. PEARL (Liu et al., 2023) formalizes cross-task preference alignment via Gromov–Wasserstein optimal transport: human-labeled preferences in a source task are transferred to the target domain by aligning trajectory spaces and computing weighted aggregates of labels. Robust reward learning then fits a probabilistic (Gaussian) model to transferred pseudo-labels, integrating the transferred uncertainty.

Zero-shot cross-lingual alignment is achieved by training a reward model on human preference data in one source language and directly applying it to target languages without further adaptation (Wu et al., 18 Apr 2024). The same model architecture (e.g., mT5-XL, PaLM-2-XXS with a scalar RM head) and tokenization scheme support the transfer, exploiting the common representation space. This transfer sometimes results in better alignment than same-language training, with empirical win rates exceeding 70 %, as cross-lingual RMs regularize away language-specific artifacts.

5. Operator-Theoretic and Embedding-Based Zero-Shot Reward Modeling

Zero-shot reward transfer in RL is formally characterized by learning an operator that maps any reward function $r$ to the correct value function $q_\pi^r$ or $q_*^r$ (Tang et al., 2022, Ollivier, 15 Feb 2025). Operator deep Q-learning constructs attention-based and linear-decomposition networks that evaluate and optimize value functions for arbitrary rewards by combining reference points sampled from offline data and theoretical resolvent operator properties. Once trained offline, such networks enable policy optimization or evaluation for any new reward in a strictly zero-shot manner.

Embedding-based frameworks, notably successor features (SF), forward–backward (FB) representations, and distributional successor features (DiSPO), encode long-term dynamics and occupancy information (Touati et al., 2022, Zhu et al., 10 Mar 2024). FB models jointly learn both state features and successor features, producing robust zero-shot performance across RL benchmarks. DiSPO employs diffusion models to learn distributions over successor features, supporting sample-based or gradient-guided inference for arbitrary new linear rewards.

6. Experimental Validation and Limitations

Zero-shot reward models have been empirically validated across diverse RL tasks:

VLM-RMs reach 100% human-evaluated success for humanoid pose imitation (kneeling, lotus position), with strong scaling effects as model size increases (Rocamonde et al., 2023).
RLCF yields substantial improvements for vision-language classification (+11.1% OOD accuracy), retrieval, and captioning, with no labeled test-time data (Zhao et al., 2023).
PEARL achieves ≈93.2% of oracle performance in robotic manipulation without any target-task human labels, and remains robust to noisy preference transfer (Liu et al., 2023).
LLM-driven RLAIF and hybrid frameworks match or exceed unbiased human feedback even when human labels are adversarially biased (Nazir et al., 26 Mar 2025).
Operator nets and FB models attain 85% of fully supervised RL performance for immediate zero-shot reward transfer in offline policy evaluation and optimization (Tang et al., 2022, Touati et al., 2022).
Cross-lingual RMs regularly outperform monolingual baselines, delivering ≥70% win rates in human A/B testing (Wu et al., 18 Apr 2024).

Noted limitations include:

Pretrained model sensitivity to domain shift, spatial reasoning, and coverage gaps.
Reward ambiguity when desired goals are abstract, poorly defined, or multi-modal.
Scaling challenges for optimal transport and embedding-based transfer in high-dimensional state spaces.
Computational overhead, especially in large-scale RL (e.g., 600K PPO episodes for dialog personalization).
Dependence on the expressiveness of pretrained encoders and the quality of offline datasets.

7. Future Directions

Research on zero-shot reward models is rapidly expanding, with promising directions including:

Multi-goal and ensemble opposite-reward design for broader safety and compositionality (Ye et al., 27 Mar 2024).
Automated or adaptive prompt engineering for VLM and LLM reward models (Kwon et al., 2023).
Integration of multi-modal critics (vision, text, audio) and fine-grained sequence-level or token-level rewards (Gallego, 2023).
Kernelization and hierarchical embeddings to expand the universality and coverage of operator- and feature-based models (Ollivier, 15 Feb 2025).
Robustness and auditing for manipulative or misaligned critics, especially as LLMs and VLMs become more widely used for sensitive or high-stakes deployments.

The field is converging toward scalable frameworks where reward signals—once a major bottleneck—are replaced by flexible, transferable, and immediately actionable scoring functions from universal pretrained models or theoretical representations. This enables rapid generalization and alignment across new domains, user populations, languages, and tasks.