NeoHorse-1-9B

NeoHorse-1-9B

Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness.

GitHub ModelScope Hugging Face Company Twitter / X License: Apache-2.0

Technical Report

NeoHorse-1-9B is a 9B causal language model and an initial prototype on the path toward recursive self-improvement (RSI). It is post-trained from Qwen3.5-9B for text-based agent harnesses, tool use, coding, and instruction following.

Derived from Qwen/Qwen3.5-9B and fine-tuned by TokenRhythm. This release contains language-model weights only and is repackaged for text-only inference. Vision weights are not included. Repackaging changes configuration and tensor key names, without changing the fine-tuned tensor values.

Highlights

• Path toward RSI: the routing harness assigns tasks to a heterogeneous model pool, records tool interactions and outcomes, estimates capability demand, and uses capability-level feedback to shape the next training mixture. Updated models can return to the harness, closing a prototype evaluation–selection–update loop; extending this loop across successive iterations is the next step toward RSI.
• Agentic post-training framework: the associated research explores routing-guided curriculum SFT and routing-guided on-policy distillation to turn execution trajectories into training signal while preserving execution and harness context around each response.
• Data quality: exact and near-duplicate removal, evaluation decontamination, structural validation, six-dimensional semantic evaluation, and subscene-level Scene/Goal/Outcome labeling.
• Broad gains: 69.04 macro average across ten benchmarks versus 65.60 for Qwen3.5-9B (+3.44).

Model Details

Property Value

Model family NeoHorse Agent-Native Causal Language Model

Parameters Approximately 9B

Base model Qwen3.5-9B

Post-training Routing-guided agentic post-training

Interface Text input and text output

Context length 262,144 natively and extensible up to 1,010,000 tokens.

Weight format / precision Safetensors / BF16

Evaluation

The 9B track compares NeoHorse-1-9B with five representative open-weight baselines: Granite-4.2-8B, Qwen3.5-9B, Ornith-1.5-9B, Gemma-4-12B-it, and Muse-Glimmer-30B. Results cover ten benchmarks and are grouped by capability. Higher is better; Δ is NeoHorse-1-9B minus Qwen3.5-9B. Bold and underline mark the best and second-best results in each benchmark row, respectively; ties share the same formatting.

Benchmark Granite-4.2-8B Qwen3.5-9B Ornith-1.5-9B Gemma-4-12B-it Muse-Glimmer-30B NeoHorse-1-9B Δ vs Qwen3.5-9B

🤖 Agentic

QwenClawBench

37.01

44.04

47.27

43.53

46.11

48.73

+4.69

WorkBuddy Bench

35.07

39.60

29.29

29.65

45.85

40.15

+0.55

PinchBench

56.93

74.55

68.22

58.89

71.35

82.25

+7.70

VitaBench

23.00

31.25

26.75

36.50

48.50

42.25

+11.00

BFCL v4

52.06

64.88

65.03

62.06

53.74

67.43

+2.55

tau2-Bench

62.28

88.04

83.68

59.37

76.64

90.82

+2.78

💻 Coding

HumanEval

96.34

92.68

93.90

100.00

98.17

98.17

+5.49

LiveCodeBench v6

72.00

65.14

47.43

73.14

65.71

65.14

+0.00

📚 Instruction Following

IFBench

78.00

66.33

40.00

77.67

78.67

66.33

+0.00

IFEval

92.98

89.46

71.35

94.27

93.90

89.09

-0.37

📊 Overall

Ten-benchmark average

60.57

65.60

57.29

63.51

67.86

69.04

+3.44

Reported protocol: SGLang v0.5.17 · temperature=1.0 · top_p=0.95 · top_k=20 · min_p=0.0 · presence_penalty=1.5 · repetition_penalty=1.0 · thinking mode enabled with enable_thinking=true and force_nonempty_content=true. QwenClawBench, WorkBuddy Bench, and tau2-Bench use three runs; PinchBench and VitaBench use one run; the remaining benchmarks follow their official protocols. VitaBench uses the DeepSeek-V4-Flash simulator and judge.

Deployment

The examples below are for self-hosted deployment from a downloaded local checkpoint.

Local checkpoint path

The examples below assume the checkpoint has already been downloaded to local disk. Set MODEL_PATH to the directory containing config.json, tokenizer files, and model weights.

MODEL_PATH="/path/to/NeoHorse-1-9B"

The OpenAI-compatible requests below use the server's --served-model-name (for example, neohorse-1-9b), not the filesystem path.

SGLang

The technical report uses SGLang v0.5.17.

pip install "sglang==0.5.17"
MODEL_PATH="/path/to/NeoHorse-1-9B"
python3 -m sglang.launch_server \
  --model-path "$MODEL_PATH" \
  --served-model-name neohorse-1-9b \
  --host 0.0.0.0 \
  --port 30000 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

Send an OpenAI-compatible request after the server starts:

curl http://localhost:30000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"neohorse-1-9b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'

vLLM

pip install -U vllm
MODEL_PATH="/path/to/NeoHorse-1-9B"
vllm serve "$MODEL_PATH" \
  --served-model-name neohorse-1-9b \
  --host 0.0.0.0 \
  --port 8000 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

The server exposes an OpenAI-compatible /v1/chat/completions endpoint. Send a request after the server starts:

curl http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"neohorse-1-9b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'

The example uses the configured 262,144-token context limit. Actual capacity depends on GPU memory and serving settings; reduce the context limit if needed.

License

NeoHorse-1-9B is released under the Apache License 2.0.

The upstream model is Qwen/Qwen3.5-9B. Its original copyright notice, Copyright 2026 Alibaba Cloud, is retained in the license file. TokenRhythm has modified the model through fine-tuning and repackaging for text-only inference. Modification notices are included in this model card and the released configuration, weight index, and Safetensors metadata.

Citation

@misc{neohorse2026,
  title        = {NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness},
  author       = {NeoHorse Team},
  year         = {2026},
  howpublished = {arXiv preprint},
  eprint       = {2609.08183},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url          = {https://arxiv.org/abs/2609.08183}
}

For questions or issue reports, use the NeoHorse project repository.