Most model improvement pipelines treat user interactions as isolated examples. NeoHorse-1 reframes agent executions as structured curriculum signal: by recording routing decisions, tool calls, and outcomes, it converts real harnessed agent trajectories into validated training examples and then reintroduces updated models into the same harness to close a feedback loop.
Key Findings
- Routing-guided curriculum and on-policy distillation: routing signals organize supervised fine-tuning into a three-stage curriculum and supervise student responses under the same progression, preserving interleaved reasoning and tool context.
- Evaluation-selection-update loop: capability-guided allocation turns evaluation feedback into the next training mixture, creating a prototype closed loop for iterative improvement (a step toward recursive self-improvement).
- Empirical gains: post-training raises the ten-benchmark macro-average from 58.94 to 64.87 for the 4B model and from 65.60 to 69.04 for the 9B model, narrowing the gap between model sizes across agent, coding, and instruction-following tasks.
- Data & quality controls: heterogeneous model pool routing, structural validation, six-dimensional semantic evaluation, subscene-level labeling, and decontamination to preserve execution context while reducing noise.
Who it's for and trade-offs
Great fit if you run an agent harness or multi-model routing infrastructure and want to turn real executions and tool use into training signal without losing execution context. Look elsewhere if you need plug-and-play performance gains without building routing/evaluation pipelines: the approach requires investment in routing instrumentation, evaluation labeling, and curriculum design, and the paper presents an initial prototype rather than a turnkey solution.
Methodology overview
The system pairs a heterogeneous model pool with intelligent routing that records predicted capability demand and chosen service tier per turn. These traces are validated, semantically evaluated, labeled at subscene granularity (Scene/Goal/Outcome), and mixed into routing-guided SFT and on-policy distillation schedules; updated models can re-enter the harness to form repeated evaluation-selection-update iterations.