Episode
NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
- Podcast
- Daily Paper Cast
- Published
- Sep 9, 2026
- Duration seconds
- 1197
- Processing state
not_requested- Canonical source
- https://share.transistor.fm/s/abdae4bf
Actions
POST https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/neohorse-1-towards-recursive-self-improvement-via-agentic-post-training-with-routing-harness/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/daily-paper-cast-7079649/neohorse-1-towards-recursive-self-improvement-via-agentic-post-training-with-routing-harness.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
🤗 Upvotes: 134 | cs.CL Authors: NeoHorse Team, Guoliang Cao, Guohao Dai, Tianyu Guo, Kai Han, Hailin Hu, Zihan Jiang, Xiang Kuang, Boxun Li, Yulong Li, Zehua Pei, Yuchuan Tian, Jiamin Wang, Yu Wang, Yunhe Wang, Yihong Wu, Haiyang Xu, Shuo Zhang, Hang Zhou, Siyang Cheng, Jiayu Fan, Wei He, Qingrui Jiao, Hongguang Li, Zhiyuan Li, Runke Liu, Xi Liu, Xinchen Liu, Sinno Jialin Pan, Yi Ren, Liuyang Song, Chenyu Wang, Bei Yu, Quanlu Zhang, Xiangyu Zhang, Mengyu Zheng, Yingjie Zong Title: NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness Arxiv: http://arxiv.org/abs/2609.08183v1 Abstract: Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-native models developed to explore this path through agentic post-training. Our system combines a heterogeneous model pool with intelligent routing, recording the predicted capability demand, selected service tier, and subsequent interaction for each user turn. These records are converted into training examples that preserve interleaved reasoning, tool calls, and harness context, and are admitted through structural validation, six-dimensional semantic evaluation, and subscene-level labeling. Routing signals organize supervised fine-tuning into a three-stage curriculum and extend to routing-guided on-policy distillation, where a teacher supervises student-generated responses under the same progression. Capability-guided allocation then converts evaluation feedback into the next training mixture, closing an evaluation-selection-update loop in which what the system learns to do shapes what it learns from next. Across eleven benchmarks cover…