From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement
The paper claims open-ended LLM training can get verifiable rewards by turning the task into a game-like proxy.
RLSVR reframes tasks so the environment itself produces the reward, avoiding human preference labels, reward models, or LLM judges. Its SpyRL setup has agents complete the same task with asymmetric information, then vote to find a preset “spy,” making the reward checkable. The authors report gains on summarization, creative writing, and math reasoning over existing self-improvement methods. HF Daily Papers' note
RLSVR reframes tasks so the environment itself produces the reward, avoiding human preference labels, reward models, or LLM judges. Its SpyRL setup has agents complete the same task with asymmetric information, then vote to find a preset “spy,” making the reward checkable. The authors report gains on summarization, creative writing, and math reasoning over existing self-improvement methods. HF Daily Papers' note
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