NeoHorse Team presents NeoHorse-1 for recursive self-improvement
Presented NeoHorse-1, a family of agent-native models that aims to implement recursive self-improvement (RSI) through agentic post-training. Combining a heterogeneous model pool with intelligent routing, it records for every user turn the predicted capability demand, the selected service tier and the follow-up interaction, turns these into training examples that preserve reasoning, tool calls and harness context, and adopts them after structural checks, a 6-dimension semantic evaluation and sub-scene-level labeling. It organizes SFT into a 3-stage curriculum using routing signals and extends it with routing-guided on-policy distillation, closing the evaluate-select-update loop. The authors claim that the macro average across 11 benchmarks, including harness-based agents, tool use, coding and instruction following, rose from 58.94 to 64.87 for the 4B model and from 65.60 to 69.04 for the 9B model.
Why it matters
A structure that feeds the routing logs generated during serving back in as the next training data — an early prototype of a self-improvement loop in which the boundary between deployment and training disappears.
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2609.08183Added in Week 38, 2026 · See that week