🚀 SPARKLE preprint is now live on arXiv! Reinforcement learning has driven impressive gains in LLM reasoning—but what exactly does RL improve? SPARKLE answers this question with a fine-grained evaluation framework that dissects reasoning into plan-following, problem decomposition, and knowledge use.

The results are surprising: explicit plans can actually hurt on the hardest problems, yet RL-tuned models remain far more robust and flexible in handling them. We also find clear gains in how RL enhances knowledge integration.

And we push back on a common myth: hard problems can be useful for RL—even when they seem unrewarding. SPARKLE shows how to turn those tough cases into real training signal.