RLSVR Framework: Self-Verifiable Rewards for Open-Ended Tasks
机器之心 · wechat · 2026-08-06
Reinforcement Learning with Verifiable Rewards (RLVR) excels in deterministic tasks like math and coding but struggles with open-ended tasks like creative writing. Inspired by self-supervised learning, researchers proposed the RLSVR framework, which transforms open-ended tasks into proxy tasks with hidden variables and deterministic rules, automatically generating verifiable reward signals.
Building on this, the authors developed SpyRL (Self-Play Reinforcement Learning). It constructs an information-asymmetric multi-agent game environment similar to "Spyfall": civilians receive full information, while the spy gets degraded info. Models judge each other's identities based on outputs. Since the identity is preset by the environment, the judgment result serves directly as an RL reward. Poorer outputs are easier to detect, cleverly mapping open-ended quality assessment to precisely verifiable game outcomes.
Experiments show that SpyRL significantly improves text summarization, creative writing, and math reasoning on Qwen3-4B and 8B, without relying on human annotation or external LLM judges, while also demonstrating positive cross-task transfer.
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