RLVR's verifier bottleneck: four research routes to extend verifiable rewards beyond closed tasks
机器之心 · wechat · 2026-09-06
A Machine Heart PRO deep-dive maps the core bottleneck of RLVR (Reinforcement Learning with Verifiable Rewards) since 2025. Exemplified by DeepSeek-R1 and OpenAI o3, RLVR replaces learned reward models — prone to noise and reward hacking — with deterministic verifiers that judge answers via rules, ground truth, or unit tests.
Two limits are emerging: coverage (open-ended writing, dialogue, medical QA lack verifiable answers) and granularity (binary signals can't capture partial correctness, leading to sparse training signals — e.g., GRPO groups where all responses are right or wrong yield zero advantage).
In 2026, the focus is shifting from scaling RL to expanding verification, along four routes: process verification (rewarding intermediate steps), signal mixing, self-verification, and rubric compilation for open-ended tasks — each attacking the problem from a different dimension: what is verified, where signals come from, who verifies, and how tasks are framed.
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