Paper Proposes RLSVR: Creating Verifiable Rewards Through Task Design for RL
zhaoran_wang · x · 2026-08-11
The paper From RLVR to RLSVR introduces a new approach to self-improvement: while open-ended tasks may not be inherently verifiable, verifiability can be engineered through task design.
The core framework, RLSVR (RL with Self-verifiable Reward), transforms the original task into a proxy environment where rewards are automatically verifiable. The authors instantiate this idea with a self-play game called SpyRL:
- 🎭 One agent is secretly designated as the 'spy' and receives degraded information, while others get the full input.
- 🗳️ A group of agents collaboratively solves the task and then votes to identify the spy.
- ✅ Because the spy's identity is predetermined, the reward is verifiable by construction.
Training with this method demonstrably improves model performance on summarization, creative writing, and math. It presents an interesting connection between self-supervised learning and RL post-training.
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