RLSVR Extends Verifiable RL to Open-Ended LLMs

Reinforcement Learning with Self-Verifiable Rewards (RLSVR) tackles a core bottleneck in LLM self-improvement: while traditional Reinforcement Learning with Verifiable Rewards (RLVR) relies on deterministic rules in domains like math and coding, open-ended tasks like creative writing and summarization lack objective ground truth. RLSVR converts open-ended generation into multi-agent proxy environments with objective, rule-verifiable outcomes—such as the "Who's the Spy?" game in its SpyRL implementation—allowing LLMs to generate self-verifiable reward signals without expensive human feedback or biased LLM judges.