Principle:Alibaba ROLL Verifiable Reward Computation

Knowledge Sources	RLVR DeepSeek Math Alibaba ROLL
Domains	Reinforcement_Learning, Reward_Modeling
Last Updated	2026-02-07 20:00 GMT

Overview

A reward computation principle that uses rule-based verification to score LLM-generated responses against ground-truth answers across multiple domains.

Description

Verifiable Reward Computation replaces learned reward models with deterministic, rule-based verification. Instead of training a neural reward model (which can be exploited through reward hacking), verifiable rewards compare generated answers against known correct answers using domain-specific verification logic:

• Math domain: Symbolic equivalence checking using SymPy (e.g., verifying that "x=2" and "2" are equivalent answers)
• Code domain: Sandboxed execution with test case verification using Docker containers
• General reasoning: Pattern matching and instruction following checks (IFEval rules)
• LLM-as-Judge: Using a separate LLM to evaluate response quality

This approach provides more reliable training signals for RL because rewards are exact (binary or discrete) rather than noisy estimates from a learned model.

Usage

Use this principle when training LLMs on tasks with verifiable answers (mathematics, coding, instruction following). The reward computation is domain-routed: each domain's reward worker handles its own verification logic.

Theoretical Basis

The reward function provides the signal for policy gradient optimization:

$r(s,a)=\{\begin{array}{ll}1& \text{if }\text{verify}(a,a^{*})=\text{True}\\ 0& \text{otherwise}\end{array}$

Where $a$ is the generated response and $a^{*}$ is the ground truth.

Additional signal shaping includes:

• Repetition penalty: Penalizes n-gram repetition in responses
• Format rewards: Bonus for following expected output format
• Difficulty weighting: Adjusts reward impact based on problem difficulty

Related Pages

Implemented By

• Implementation:Alibaba_ROLL_MathRuleRewardWorker_Compute_Rewards

Related Heuristics

The following heuristics inform this principle:

• Heuristic:Alibaba_ROLL_Reward_Clipping_Normalization