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AI Research
arXiv CS.AI·May 25·5 min read
VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
TL;DR
Reinforcement Learning with Verifiable Rewards (RLVR) struggles with scalability due to its dependence on external verifiers. VI-CuRL, or Verifier-Independent Curriculum Reinforcement Learning, was developed to stabilize training by using the model's intrinsic confidence to prioritize high-confidence samples.
✦ Why It Matters
Engineers can leverage VI-CuRL to enhance the stability and performance of reinforcement learning models without relying on external verifiers.
Key Takeaways
- Implement VI-CuRL to improve model stability in RL tasks.
- Focus on high-confidence samples to enhance learning efficiency.
- Utilize the theoretical framework for unbiased estimators in RL.
- Explore verifier-independent methods for scalable AI solutions.
Full Summary
Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning in Large Language Models (LLMs) but is limited by its reliance on external verifiers, which hinders scalability. To overcome this, VI-CuRL (Verifier-Independent Curriculum Reinforcement Learning) was introduced, focusing on the model's intrinsic confidence to create a curriculum that does not depend on external validation.
By prioritizing high-confidence samples, VI-CuRL effectively addresses the bias-variance trade-off, particularly reducing action and problem variance. The authors provide a theoretical analysis demonstrating that their estimator maintains asymptotic unbiasedness.
Empirical results show that VI-CuRL promotes stability and consistently outperforms both verifier-dependent and independent baselines across math and general reasoning tasks. This advancement suggests a promising direction for developing more robust reinforcement learning algorithms without the need for external verification.