Why Reward Models Need Reasoning: From Scalar Scores to RM‑R1

1. The Black‑Box Problem of Traditional Reward Models

Traditional reward models (ScalarRM) are trained on large numbers of preference pairs, learning to assign a single numeric score to each answer. For example: (A is better than B) After training the model produces scores such as:

A → score = 0.86
B → score = 0.73

Although this seems straightforward, it creates several critical issues in practice.

Problem 1: No Explanation

The scalar output tells *what* is better but not *why* it is better. When the model outputs 0.86, it cannot explain whether the answer is better because of logic, style, politeness, or length. This lack of reasoning means the reward cannot guide the policy model on how to improve, only how to be penalized.

Problem 2: Instability and Reward Hacking

Scalar rewards are often unstable. The same question may receive contradictory rankings because the underlying embedding‑plus‑linear‑head architecture has shallow dimensions, low information density, and is sensitive to length or corpus bias. Reward hacking occurs when longer, more verbose answers receive higher scores regardless of quality.

Problem 3: Blind Policy Learning

During PPO/GRPO optimization, the policy receives signals like "this answer is good, that answer is bad" without any insight into *why* the judgment was made. Consequently, the policy may generate formatted but shallow answers, mimic logical rigor without substance, or converge to a single style, leading to higher scores but poorer task capability.

2. From Scoring to Reasoning: The Alignment Mechanism

Two directions address these problems:

Enhance the expressive power of scalar scores (found ineffective in practice).

Change the reward model from a pure judge to a reasoning‑based evaluator (RM‑R1).

RM‑R1 treats the reward model as a teacher that first makes explicit the evaluation criteria (rubrics), then provides a step‑by‑step analysis, and finally issues a verdict.

<rubric>
  Evaluate logic, factuality, politeness, task completion…
</rubric>

<analysis>
  Analyze answer A dimension by dimension.
  Analyze answer B dimension by dimension.
</analysis>

<verdict> A is better </verdict>

This approach yields three key capabilities:

Capability 1: Standard‑Based Decision

The reward model makes its preference structure explicit, turning the black box into an explainable system.

Preferences become tunable and adjustable.

Practitioners can see exactly what the model is teaching.

Capability 2: Task Understanding and Reasoning

Earlier RMs acted as blind judges that knew the task only superficially. RM‑R1 equips the reward model with:

Task comprehension

Logical reasoning

Background modeling

Dimensional awareness

Thus the reward model becomes a "task‑learned expert judge" rather than an external observer.

Capability 3: Knowledge Transfer to the Policy Model

Because the reward now includes a reasoning trace, the policy model learns not only the final answer but also the underlying reasoning structure.

Why do I say A wins?
- Fact check: A mentions risk assessment, B does not.
- Task match: A provides actionable advice, B only offers empathy.
- Empathy: B is more emotional, but lacks guidance.
Conclusion: A is better

The reward signal thus shifts from a raw number to a "knowledge abstraction" that conveys task knowledge, enabling the policy model to improve instruction following, reasoning ability, task performance, and explainability.

3. RM‑R1 Architecture

The training pipeline forms a closed loop:

SFT: Teach the model to output rubrics, analyses, and verdicts.

RM training: Learn to evaluate preferences using the rubric‑based reasoning.

RLVR/GRPO: Let the policy model absorb the reward reasoning chain.

Policy error → refined case → preference data → RM‑R1 → finer reward → policy improvement

This loop turns the reward model from a mere judge into a teacher that conveys methodology.

4. Real‑World Example: Why Reasoning Beats Scalar Scoring

Given the user query "Should I quit my stressful job?", two models respond:

Model A: "You should carefully consider quitting and evaluate the risks..."
Model B: "Quit, life is short, don't force yourself."

A scalar RM might favor Model B because it sounds warmer, but RM‑R1 evaluates dimensions such as factuality, task relevance, and empathy, concluding that Model A is superior and explaining the reasons.

5. Why the Industry Must Adopt Reasoning‑Based Rewards

Models below GPT‑4 level cannot be aligned effectively with pure penalty signals. They need structured, explanatory feedback, reasoning chains, and transferable preference systems to achieve stable, generalizable, and task‑oriented behavior.

6. Interview‑Ready Summary

When asked why reward models need reasoning, answer: "Scalar rewards cannot establish true behavior alignment; reasoning‑based reward models make preferences explicit, provide explanatory judgments, and transfer task understanding to the policy model, turning RLHF from penalty‑driven to reasoning‑driven alignment."