NeoHorse-1

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

Technical Report

NeoHorse-1 is a family of causal language models and an initial prototype on the path toward recursive self-improvement (RSI). The 4B and 9B checkpoints are post-trained from Qwen3.5 for text-based agent harnesses, tool use, coding, and instruction following.

The routing harness assigns tasks to a heterogeneous model pool, records tool interactions and outcomes, estimates capability demand, and feeds capability-level feedback into the next training mixture. Updated models can return to the harness, forming a prototype evaluation–selection–update loop; extending this loop across successive iterations is the next step toward RSI.

Highlights

• Agentic post-training framework: routing-guided curriculum SFT and routing-guided on-policy distillation turn execution trajectories into training signal while preserving execution and harness context.
• Data quality: exact and near-duplicate removal, evaluation decontamination, structural validation, six-dimensional semantic evaluation, and subscene-level Scene/Goal/Outcome labeling.
• Two release sizes: 4B for a lighter local deployment footprint and 9B for higher capacity on the same text-first serving interface.

Model Downloads

Checkpoint	Parameters	Hugging Face	ModelScope	Base model
NeoHorse-1-4B	~4B	TokenRhythm/NeoHorse-1-4B	Coming soon	Qwen3.5-4B
NeoHorse-1-9B	~9B	TokenRhythm/NeoHorse-1-9B	Coming soon	Qwen3.5-9B

Both checkpoints are released as text input/text output language-model weights for self-hosted inference. Each model card contains its model-specific evaluation table and deployment notes.

Model Details

Property	NeoHorse-1-4B	NeoHorse-1-9B
Model family	NeoHorse Agent-Native Causal Language Model	NeoHorse Agent-Native Causal Language Model
Parameters	Approximately 4B	Approximately 9B
Post-training	Routing-guided agentic post-training	Routing-guided agentic post-training
Interface	Text input and text output	Text input and text output
Context length	262,144 natively; base capability extensible up to 1,010,000 tokens	262,144 natively; base capability extensible up to 1,010,000 tokens
Weight format / precision	Safetensors / BF16	Safetensors / BF16

Evaluation

The tables report the ten-benchmark protocol from the technical report. Results are grouped by capability. Higher is better; Δ is NeoHorse minus the same-size Qwen baseline. Bold marks the best result in each benchmark row; ties share the same formatting.

4B track

The 4B comparison includes four representative open-weight baselines.

Benchmark	Qwen3.5-4B	Gemma-4-E4B-it	Nanbeige-4.2-3B	Agents-A1-4B	NeoHorse-1-4B	Δ vs Qwen3.5-4B
🤖 Agentic
QwenClawBench	38.47	22.98	40.66	43.16	44.68	+6.21
WorkBuddy Bench	24.62	11.65	21.03	33.37	34.41	+9.79
PinchBench	71.19	47.60	66.78	75.07	77.33	+6.14
VitaBench	21.50	5.00	31.50	39.25	32.00	+10.50
BFCL v4	61.02	47.18	67.28	46.60	61.79	+0.77
tau2-Bench	84.29	43.60	85.08	81.00	88.46	+4.17
💻 Coding
HumanEval	87.20	84.76	98.78	92.68	96.95	+9.75
LiveCodeBench v6	53.71	52.00	72.50*	56.57	59.43	+5.72
📚 Instruction Following
IFBench	60.33	40.00	55.00	63.33	65.33	+5.00
IFEval	87.06	74.68	84.47	83.55	88.35	+1.29
📊 Overall
Ten-benchmark average	58.94	42.95	62.31	61.46	64.87	+5.93

9B track

The 9B comparison includes five representative open-weight baselines from the technical report. Underline marks the second-best result in each benchmark row.

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

* Nanbeige-4.2-3B LiveCodeBench v6 result is reported from its official blog post or technical report.

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

These examples serve a checkpoint downloaded to local disk. Set MODEL_PATH to the directory containing config.json, tokenizer files, and weights. The request model value is the served alias (neohorse-1-4B or neohorse-1-9B), not the filesystem path.

SGLang

pip install "sglang==0.5.17"
MODEL_PATH="/path/to/NeoHorse-1-4B"  # or /path/to/NeoHorse-1-9B
python3 -m sglang.launch_server \
  --model-path "$MODEL_PATH" \
  --served-model-name neohorse-1-4B \
  --host 0.0.0.0 --port 30000 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

vLLM

pip install -U vllm
MODEL_PATH="/path/to/NeoHorse-1-4B"  # or /path/to/NeoHorse-1-9B
vllm serve "$MODEL_PATH" \
  --served-model-name neohorse-1-4B \
  --host 0.0.0.0 --port 8000 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

The 262,144-token setting is a configured limit; actual capacity depends on GPU memory and serving settings. Reduce it if needed. These launch examples have not yet been validated on GPU for this repackaged release.

The examples below target vLLM at http://127.0.0.1:8000. For SGLang, use http://127.0.0.1:30000. For the 9B checkpoint, use --model neohorse-1-9B and launch the server with --served-model-name neohorse-1-9B.

Send a Request

Install the client dependency:

pip install requests

Run the provided chat example from the repository root:

python examples/chat.py \
  --url http://127.0.0.1:8000 \
  --model neohorse-1-4B

This example sends a predefined prompt to the deployed model and prints the generated response.

Alternatively, send an OpenAI-compatible request directly:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"neohorse-1-4B","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'

Tool Calling

Run the provided tool-calling example from the repository root:

python examples/tool_call.py \
  --url http://127.0.0.1:8000 \
  --model neohorse-1-4B

This example sends a predefined weather query and prints the tool call generated by the model.

License

NeoHorse-1 is released under the Apache License 2.0. The upstream models are Qwen3.5-4B and Qwen3.5-9B.

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}
}