SCRL: Reinventing RLVR for Tougher Problems

Reinforcement learning from verifiable rewards (RLVR) has long promised breakthroughs in large language model (LLM) reasoning. The reality is, it struggles with tough problems. Correct final-answer rollouts are scarce, and the system can't harness partial progress from failed attempts. Enter SCRL (Subproblem Curriculum Reinforcement Learning), a framework that flips this issue on its head.

Revolutionizing the Framework

SCRL introduces a novel curriculum RL framework. It breaks down complex problems into verifiable subproblems, with the final subproblem reverting back to the original issue. This approach transforms partial progress into tangible learning signals. The architecture matters more than the parameter count, as SCRL uses subproblem-level normalization. This technique normalizes rewards at each subproblem position, assigning advantages directly to corresponding answer spans. There's no need for external rubrics or reward models here.

Cracking the Code

What SCRL does is lift hard problems out of gradient dead zones. The harder the problem, the greater the relative gains. Here's what the benchmarks actually show: across seven mathematical reasoning benchmarks, SCRL outshines strong curriculum-learning baselines. It improves average accuracy over GRPO by 4.1 points on the Qwen3-4B-Base and 1.9 points on the Qwen3-14B-Base. Notably, on challenging benchmarks like AIME24, AIME25, and IMO-Bench, SCRL pushes pass@1 up by 3.7 points and pass@64 by 4.6 points on Qwen3-4B-Base.

Why SCRL Matters

Why should anyone care about this? Because it suggests a new path forward for AI reasoning. If SCRL can consistently offer better exploration on hard reasoning problems, what does this mean for the future of AI applications? Could this lead to more autonomous systems capable of handling increasingly complex tasks?

Strip away the marketing and you get a framework that potentially redefines what's possible in AI reasoning. It's a big claim, but the numbers tell a different story. SCRL isn't just an upgrade, it's a major shift in how we approach problem-solving with AI.