arXiv:2602. 14872v3 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has been a main driver of recent breakthroughs in large reasoning models.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2601. 18778v3 Announce Type: replace Abstract: RL methods for scaling large reasoning models stall on datasets with low initial success rates, and thus little training signal.
arXiv:2609.13997v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has shown remarkable success in improving the mathematical reasoning of large language models. Ye...
arXiv:2601. 15158v4 Announce Type: replace-cross Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought).
arXiv:2607. 07646v1 Announce Type: new Abstract: Does RL post-training merely amplify primitive skills already latent in a base model, or can it compose primitive skills into new higher-level strategies?
arXiv:2608. 01522v1 Announce Type: new Abstract: Teaching a language model a skill it has not mastered is obstructed by three recurring difficulties: training data is scarce, ground-truth reasoning traces are usually unavailable, and models often exhibit an apparent ceiling beyond which additional data yields no further improvement.
The paper investigates reinforcement learning with verifiable rewards (RLVR) by expanding the reasoning space beyond depth to include environment complexity and diverse reasoning forms. It introduces a synthetic knowledge‑graph environment that varies depth, complexity, and task family, revealing that joint depth‑complexity coverage outperforms single‑axis approaches, that different reasoning families behave non‑uniformly, and that uniform mixing beats staged curricula under a fixed budget. The study also shows that current off‑the‑shelf models share a deductive‑over‑abductive bias, indicating a broader gap in reasoning capabilities.