Looking ahead to 2026, AI inaccuracy is the most prominent risk that organizations aim to mitigate (McKinsey & Company, 2025). Leaders are seeking outputs that are accurate, repeatable, and reviewable against established business rules. Reinforcement learning with verifiable rewards (RLVR) is a technique that addresses such concerns, improving performance, increasing robustness, and reducing hallucinations. This article explains RLVR and its differences from reinforcement learning with human feedback (RLHF), demonstrating where each is best suited for achieving AI outcomes.
What is RLVR?
RLVR trains a model to earn a reward only when its output passes programmatic checks (Wen et al., 2025). Instead of asking humans which answer they prefer, the system samples multiple candidates, runs them through verifiers, and updates the policy toward behaviors that pass. Verifier-based rewards can incentivize correct reasoning and support evaluations that check both the answer and the chain of thought.
Common verifiers include:
- Math and logic: To verify exact numeric answers in the specified format, rewarding only exact matches.
- Unit tests for code: Compile and run to verify functional correctness, tracking pass@k over multiple samples (Chen et al., 2021).
- JSON schema plus field validators: Enforce a machine-consumable structure and cross-field constraints for downstream services.
- Link and citation resolution: Ensure that cited sources are resolved and support claims through retrieval, followed by a critique and evaluation (Asai et al., 2023).
Once verifiers are in place, RLVR provides low-variance, scalable feedback and produces audit-ready artifacts, tests, schemas, and logs that map cleanly to compliance reviews and KPI reporting (National Institute of Standards and Technology (NIST), 2023).
RLHF vs RLVR: Why RLVR is Rising
RLHF optimizes for human preferences, tone, helpfulness, and nuanced policy alignment (Ouyang et al., 2022). On the other hand, RLVR optimizes for objective correctness and format compliance by rewarding outputs that satisfy automated verifiers. As foundation models enhance performance for enterprise applications and more agentic workflows are productionized, they favor measurable, repeatable signals that scale. This makes RLVR the natural choice for any process where a verifiable check can be implemented.
Evidence from recent training efforts highlights the effect that large models trained with rule-based, accuracy-oriented rewards show substantial gains on math/coding and other verifiable tasks, e.g., DeepSeek-R1 (DeepSeek-AI et al., 2025).
The following table highlights the main factors that are driving organizations toward RLVR instead of RLHF.
RLVR for Subjective Business Use Case
Many high-value business tasks are partly subjective. Drafting a customer-support email, summarizing a policy, or writing an internal announcement rarely has a single “right” answer. However, there are still rules that must be followed, such as mandated disclaimers, tone guidelines, word limits, approved sources, and banned phrases.
RLVR helps by turning parts of the rubric into verifiable criteria. For example, a support response might require including a standard disclaimer, avoiding sensitive phrases, staying within a word limit, and referencing at least one relevant help centre article. Each rule becomes a simple automated check, and the model is rewarded only when it meets them.
Modern RLVR frameworks demonstrate how soft, model-based scoring on free-form answers can complement these binary checks. This enables systems to enforce hard constraints while also evaluating softer qualities, such as clarity or coverage (Su et al., 2025).
Real-World RLVR Use Cases
Enterprises are already applying RLVR in ways that map directly to business outcomes:
- Code generation: RLVR-trained coding models power assistants that produce runnable, test-passing code, improving first-try accuracy and reducing developer debugging time (Le et al., 2022).
- Text-to-SQL: Enterprises use RLVR-enhanced SQL generators to answer analytics queries reliably by producing executable SQL that returns correct results on the first attempt (Li et al., 2024).
- Grounded Q&A: RLVR-trained assistants deliver citation-backed answers for compliance workflows, ensuring responses are traceable and accurate (Asai et al., 2023).
- Structured data extraction: RLVR-aligned models generate schema-valid JSON, forms, and API payloads that integrate cleanly into automated pipelines with minimal manual correction.
How Data & Annotation Change with RLVR
With RLVR, the centre of gravity in your data work shifts from labeling preferences to engineering what “correct” looks like. Teams focus on building verifier assets, gold answers, unit tests, schemas, and SQL checks. They wire them into an execution harness that can run these tests and log behavior at scale.
Human experts stay in the loop to review edge cases, refine verifiers, and convert new failure modes into rules. At the same time, RLHF or supervised fine-tuning is layered on top to polish tone, clarity, and safety once RLVR has established correctness and structure.
Can RLHF and RLVR Work Together?
A hybrid approach is often most effective. RLVR encodes the non-negotiables with tests, schemas, and citation checks so the model consistently gets the facts right and adheres to required structures. RLHF then shapes how those correct outputs are delivered, tuning for clarity, empathy, and policy alignment.
For RLVR, teams supply verifiers backed by ground-truth assets. This includes unit tests with expected outputs, exact answers for math or logic problems, and schema-validated sample payloads. They also prepare expected SQL results and bundle everything into a reusable test harness that can run checks automatically at scale.
For RLHF, teams provide preference datasets and rater rubrics. Together, this approach produces outputs that are verifiably correct, consistent, and aligned with both user and policy expectations.
Power Your AI with Appen
Models require data and evaluation that can be defended in audits and production. Appen offers curated multimodal datasets, preference and safety reviews, and verifier-aligned evaluation to ensure RLVR and RLHF improve where it matters.
Partner with Appen’s experts to design evaluators, collect the right data, and operationalize RLVR alongside RLHF across your stack. Start the conversation today.
References
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