arXiv:2602.14872v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has been a main driver of recent breakthroughs in large reasoning models. Yet it remains a mystery how rewards based solely on final outcomes can help overcome the long-horizon barrier to extended reasoning. To understand this, we develop a theory of the training dynamics of RLVR for transformers on compositional reasoning tasks. Our theory shows that mixed-difficulty training naturally induces an implicit curriculum: without any explicit schedule, easier problems become learnable first an
This paper offers a theoretical explanation for the observed efficacy of Reinforcement Learning with Verifiable Rewards (RLVR) in large reasoning models, bridging a gap in understanding their training dynamics.
Understanding the 'implicit curriculum' in RLVR provides critical theoretical foundations for scaling future AI models, potentially informing more efficient and effective training methodologies for complex tasks.
The theoretical insight into how RLVR overcomes long-horizon reasoning challenges could lead to novel algorithmic designs, moving beyond purely empirical approaches to AI development.
- · AI researchers
- · Large Language Model developers
- · Companies investing in complex AI reasoning
- · AI development relying solely on heuristic/trial-and-error methods
Improved understanding of how current large reasoning models learn will accelerate their development and deployment.
New training paradigms leveraging implicit curricula could emerge, making AI models more robust and capable of tackling previously intractable problems.
This could contribute to the development of more generalizable AI that requires less explicit human guidance for complex problem-solving, impacting various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG