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RL from Verifiable Rewards (RLVR)
Trains reasoning models with reinforcement learning against automatically checkable rewards, such as whether a math answer matches ground truth or code passes its tests, rather than against learned preference models. Because the reward only scores final correctness, long chains of thought with backtracking and self-verification emerge without supervised rationales. This is distinct from RLHF, RLAIF, and DPO, which optimize human or AI preferences.
In 30 seconds
- What
- Trains a model with RL using automatically checkable rewards (math correctness, code tests) on final outputs only, allowing reasoning chains and self-verification to emerge without supervised rationales.
- When to use
- Problems with deterministic, verifiable ground truth answers where you want the model to discover its own reasoning process without labeled step-by-step solutions.
- Watch out
- Requires massive compute for sampling many candidate solutions per problem; most fail, making training expensive and slow compared to supervised approaches.
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RL from Verifiable Rewards (RLVR): Overview
Trains reasoning models with reinforcement learning against automatically checkable rewards, such as whether a math answer matches ground truth or code passes its tests, rather than against learned preference models. Because the reward only scores final correctness, long chains of thought with backtracking and self-verification emerge without supervised rationales. This is distinct from RLHF, RLAIF, and DPO, which optimize human or AI preferences.
- Automatically verifiable rewards (math or code correctness)
- No supervised reasoning traces required
- Emergent long chain-of-thought with backtracking
- Emergent self-verification and re-checking
- Group-relative policy optimization (GRPO)
- Distinct from preference-based RLHF, RLAIF, and DPO
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References
The papers, specifications, and repositories this pattern is based on.
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