BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation
cs.AI, cs.CL
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: 29 pages, 7 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Phi-4-reasoning Technical Report
- AMO-Bench: Large Language Models Still Struggle in High School Math Competitions
- Training Verifiers to Solve Math Word Problems
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
- Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs
- Reinforced Self-Training (ReST) for Language Modeling
- Textbooks Are All You Need
- Distilling the Knowledge in a Neural Network
- R-Zero: Self-Evolving Reasoning LLM from Zero Data
- O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
- Autodata: An agentic data scientist to create high quality synthetic data
- ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
- Ministral 3
- AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset
- Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
- Olmo 3
- gpt-oss-120b & gpt-oss-20b Model Card
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- OpenAI GPT-5 System Card
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