Does RL training create new reasoning skills or activate existing ones?

Two prominent claims about what RL post-training does appear contradictory:

The timing thesis: Since Does RL teach reasoning or just when to use it? and Do base models already contain hidden reasoning ability?, RL functions as a deployment optimizer. Evidence: base models outperform RLVR-trained models at high pass@k, RL-trained models show the same solution strategies as base models, and Can a single training example unlock mathematical reasoning?.

The capability thesis: Can extended RL training discover reasoning strategies base models cannot?. Evidence: ProRL shows strategies absent from any base model sample regardless of budget, while self-evolving curriculum RL breaks the boundary constraints identified by pass@k analysis (where Does RLVR actually expand what models can reason about?).

The domain-conditional resolution: Both are correct under different conditions. For standard math/code reasoning where the problem structure is well-represented in pretraining data, RL activates latent capability (timing thesis). For complex tasks requiring multi-step planning, tool coordination, or novel strategy recombination, RL may create genuinely new capability through prolonged training (capability thesis).

Supporting evidence for the conditional view:

• RLVR pass@k boundary collapse occurs on standard benchmarks (MATH, GSM8K)
• ProRL novel strategy discovery occurs on problems requiring deep planning
• SWE-RL doubles baseline on long-horizon engineering tasks — beyond activation
• Duration matters: short RLVR narrows boundaries while prolonged RL pushes through them

The practical implication: RL training investment should be calibrated to the target domain. For standard reasoning, minimal RL (even one example) suffices. For complex agentic tasks, sustained RL investment with evolving curricula is justified.