Reasoning Reward Models (ReasRMs)

• ReasRMs are reward models that generate explicit, interpretable rationales alongside preference labels to align AI systems with human values.
• They employ a self-training loop using both rationale-annotated and unlabeled data, which boosts data efficiency and reduces reliance on manual labels.
• Architectures like GRAM-R² integrate generative explanation with preference prediction, improving performance in response ranking, RLHF, and transparent decision-making.

Reasoning Reward Models (ReasRMs) are a class of reward models that explicitly leverage a reasoning process to produce reward signals, often in the form of interpretable rationales or structured explanations in addition to labels or scalar scores. ReasRMs deviate from conventional reward models—which typically predict only a scalar preference score for a candidate output—by generating explicit reward reasoning, with the goal of enhancing interpretability, generalization, and data efficiency in aligning LLMs and other complex AI systems with human preferences. GRAM-R$^2$ exemplifies recent progress in this area through its self-training methodology, generative model design, and ability to utilize unlabeled data for scalable reward reasoning (Wang et al., 2 Sep 2025).

1. Core Concept and Innovation

GRAM-R$^2$ introduces a self-training framework engineered to inject explicit reward reasoning into generative reward models. Unlike discriminative models that estimate a real-valued or binary reward, GRAM-R$^2$ generates both a natural language rationale (reward reasoning) and an associated preference label for a candidate or pair of candidate outputs. The self-training protocol is bootstrapped with a small, rationale-annotated dataset; a preference-proving model is first trained to generate rationales (proofs) for labeled data. For unlabeled or label-only data, the preference-proving model synthesizes reward rationales, effectively converting the dataset into a rationale-rich corpus.

The self-training loop iterates: pseudo-preference labels are generated on unlabeled samples using the reward model; corresponding rationales are synthesized by the preference-proving model; the expanded set of (input, candidates, rationale, preference) tuples is used to retrain the reward model, reinforcing both the ability to generate reward reasoning and robust preference assignment. The central training objective is: $$\mathcal{L}_{g} = - \mathbb{E}_{(c,x,y_a,y_b,l,z) \sim \mathcal{D}_p} \Bigl[ \log \pi_{\phi}(z \mid s) + \log \pi_{\phi}(w = l \mid s, z) \Bigr],$$ where $s$ is the input prompt, $z$ is the generated rationale, $l$ is the preference label, and $\pi_{\phi}$ denotes the generative model distribution.

This dual-focus loss enforces the simultaneous generation of an informative reward rationale $z$ and a reliable prediction $l$ grounded in $s$ and $z$.

2. Model Architecture

GRAM-R$^2$ is implemented as a generative LLM with architectural modifications to support multi-output generation. In contrast to classic reward models—where a regression head outputs a real-valued score for each candidate—GRAM-R$^2$’s decoder produces a formatted textual rationale (reward reasoning) concatenated with an explicit preference label. This architecture enables the model to leverage and explain its internal scoring calculus, yielding both interpretability and modularity (e.g., for integration into ranking or RL pipelines).

This reasoning-first design supports a broad range of downstream scenarios: the model can produce detailed explanations of reward assignments; facilitate in-context comparisons for response ranking; and serve as a plug-in for reinforcement learning from human feedback where calibrated, verifiable reward explanations are valuable.

3. Training Process and Data Efficiency

The GRAM-R$^2$ training sequence consists of two principal stages:

1. Pre-training with Rationale Distillation: A compact rationale-annotated dataset is used to train a “preference-proving” model capable of emitting natural language proofs justifying preferences. This model is then used to generate rationales (proofs) for rationale-free labeled data and large-scale unlabeled data, scaling up training resources without the need for expensive manual annotation.
2. Iterative Self-Training Loop: At each iteration, the generative reward model predicts preference labels on pooled unlabeled data. The preference-proving model then provides rationales for these pseudolabeled pairs. The synthesized (input, candidate outputs, rationale, label) tuples are incorporated into the training set for further fine-tuning. Gradual expansion and correction of the rationales (proofs) improves the internal consistency and expressivity of the generative model.
3. Task-Specific Fine-Tuning: For adaptation to new domains, the model can be further fine-tuned on limited task-specific preference data, leveraging its foundational capacity for reward reasoning and rationale generation.

This data-efficient protocol allows the model to scale up supervision via self-training, reducing dependence on large quantities of manually labeled rationales.

4. Applications and Performance

GRAM-R$^2$ supports a range of high-value AI alignment tasks:

• Response Ranking:

The model outputs a ranked list of candidate responses, each accompanied by a preference rationale and label. This is beneficial for reranking in LLM pipelines, where explainable reasoning about preference ordering is increasingly required.

• Task Adaptation and Transfer:

The ability to fine-tune on small amounts of domain-specific data, while retaining general reward reasoning capability, allows rapid adaptation to novel tasks such as STEM problem-solving or code generation.

• Reinforcement Learning from Human Feedback (RLHF):

GRAM-R$^2$ can be employed as the reward signal within PPO-based RLHF, providing both a preference label and a detailed explanation for each candidate, thereby improving feedback quality.

Empirical evaluation demonstrates that GRAM-R$^2$ surpasses discriminative and generative baselines on standardized tests such as RM-Bench and JudgeBench, with several percentage point improvements in performance. These gains primarily result from the explicit incorporation of reward reasoning and the effective utilization of unlabeled data through the self-training protocol (Wang et al., 2 Sep 2025).

5. Implications for the Field of Reasoning Reward Models (ReasRMs)

GRAM-R$^2$ represents a significant advancement for ReasRMs by:

• Promoting the integration of explicit, verifiable reasoning in the reward modeling process, yielding increased interpretability—a property crucial for transparent AI alignment.
• Demonstrating that the reliance on expensive rationale-based supervision can be mitigated by leveraging self-training and unlabeled data, expanding the scalability of generalist reasoning reward models.
• Providing architectural and methodological templates for future generalist, multi-task reward models that exhibit robust alignment with human preferences and superior ability to explain their judgments.
• Enabling new research directions in reward reasoning by unifying generative explanation and preference labeling within a single model. The explicit reward rationales facilitate post hoc analysis, model debugging, and further exploration into explainable AI alignment methodologies.

6. Summary Table: Distinctive Features of GRAM-R$^2$

Feature	Description	Significance
Rationale Generation	Produces natural language reward explanations with each assignment	Increases interpretability and supports post hoc inspection
Self-Training Loop	Iteratively augments data via pseudo-labels and synthesized proofs	Reduces need for manual annotations, scales supervision
Generative Architecture	Outputs jointly both rationales and preference labels	Supports ranking, RLHF, and adaptation tasks
Data Efficiency	Leverages unlabeled data for rationale synthesis and retraining	Makes broad task adaptation feasible
Alignment Performance	Surpasses strong baselines by several percentage points on benchmarks	Demonstrates value of explicit reward reasoning

This class of reasoning-centric reward models is poised to become a foundation for robust, transparent, and scalable alignment of complex AI systems, with the potential to influence best practices for preference modeling, RLHF pipelines, and interpretable evaluation in both narrow and generalist applications (Wang et al., 2 Sep 2025).