Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models

OpenAI o1-mini

Advancing cost-efficient reasoning

BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation

BoundaryEvo introduces a two‑stage data‑model co‑evolution framework for distilling long‑chain reasoning from large teacher models into small language models. First, Capability Priming distills a small set of teacher trajectories to give the student an initial reasoning baseline. Then, Capability‑Boundary Co‑Evolution iteratively selects questions the student has not mastered but the teacher can solve, updating the training data as the student’s capability grows. With only 1,000 examples, BoundaryEvo improves Qwen3.5‑4B accuracy from 62.9 % to 75.3 % on IMO‑AnswerBench and from 26.8 % to 46.0 % on AMO‑Bench, outperforming static and dynamic baselines across 11 benchmarks.

STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes

arXiv:2605.13165v2 Announce Type: replace Abstract: Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is espe...

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces

arXiv:2606. 06840v1 Announce Type: cross Abstract: Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevant options from hundreds of thousands to millions of candidate labels.

Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models

Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models