RLSVR: Self-Verifiable Rewards for Open-Ended LLM Self-Improvement

Researchers have introduced Reinforcement Learning with Self-Verifiable Rewards (RLSVR), a novel training framework that enhances RLVR for open-ended tasks by converting them into verifiable proxy environments. While RLVR has significantly advanced reasoning-focused LLMs, its applicability is restricted to areas such as mathematics and programming, where correctness can be definitively verified. In contrast, open-ended tasks depend on human preferences, reward models, or evaluations from LLMs, which can lead to bias and increased costs. RLSVR leverages self-supervised learning to create pretext tasks that generate supervision from the data itself, producing reward signals automatically through internal rules and the results of interactions. The full paper can be found on arXiv.