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44fda62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # Nexus-Coder-Alpha
A practical training guide and recipe for building state-of-the-art **agentic coding assistants** with open-source 8B parameter models.
## What This Is
This repository consolidates research from **Nemotron-Terminal**, **Klear-AgentForge**, **GLM-5**, and **Qwen3-Coder-Next** into a single reproducible training pipeline:
1. **Supervised Fine-Tuning (SFT)** on high-quality multi-turn agent trajectories
2. **Reinforcement Learning (RL)** with execution-verified rewards
3. **Deployment** in Pi agent, Cline, OpenCode, or any OpenAI-compatible coding tool
## Target Model
**Base:** [`nvidia/Nemotron-Terminal-8B`](https://hf.co/nvidia/Nemotron-Terminal-8B)
- 8.2B parameters, Qwen3 architecture, native `tool_calls` support
- Already pre-trained for terminal/code-agent interaction
- Fits on single A100 or A10g-large with LoRA
## Key Results (from cited papers)
| Benchmark | 8B Target | SOTA Reference |
|---|---|---|
| SWE-bench Verified | 20-40% | Klear-AgentForge: **39.4%** |
| BFCL v3 | 65-75% | Klear-AgentForge: **71.5%** |
| Terminal-Bench 2.0 | 15-25% | Nemotron-T-14B: **20.2%** |
| Aider-Polyglot | 25-40% | Klear-AgentForge: **33.8%** |
## Documents
- **[TRAINING_GUIDE.md](TRAINING_GUIDE.md)** — Full SFT → RL → Deployment recipe with code snippets, dataset links, hyperparameters, and SOTA tricks
- **[train_sft.py](train_sft.py)** — Reference training script for Stage 1 (SFT)
- **[train_grpo.py](train_grpo.py)** — Reference training script for Stage 2 (GRPO RL)
## Quick Start
```bash
# Stage 1: SFT on curated agent trajectories
python train_sft.py \
--model nvidia/Nemotron-Terminal-8B \
--dataset mixed_agentic_dataset \
--output_dir ./nexus-coder-sft
# Stage 2: GRPO with execution-verified rewards
python train_grpo.py \
--model ./nexus-coder-sft \
--dataset nvidia/Nemotron-RL-Agentic-SWE-Pivot-v1 \
--output_dir ./nexus-coder-rl
```
## Core Datasets
| Dataset | Split | Purpose | Link |
|---|---|---|---|
| SWE-bench/SWE-smith-trajectories | `tool` (resolved=True) | SFT: Real repo bug fixing | [HF](https://hf.co/datasets/SWE-bench/SWE-smith-trajectories) |
| nvidia/Nemotron-Agentic-v1 | `interactive_agent` + `tool_calling` | SFT: Multi-turn tool use | [HF](https://hf.co/datasets/nvidia/Nemotron-Agentic-v1) |
| xingyaoww/code-act | `codeact` + `general` | SFT: Executable code actions | [HF](https://hf.co/datasets/xingyaoww/code-act) |
| nvidia/Nemotron-RL-Agentic-SWE-Pivot-v1 | `train` | RL: Step-level pass-rate rewards | [HF](https://hf.co/datasets/nvidia/Nemotron-RL-Agentic-SWE-Pivot-v1) |
## Top SOTA Tricks
1. **Multi-format tool templates** — Train on 4-5 schemas (OpenAI JSON, XML, Python-style, TypeScript, Qwen3-native) so the model generalizes to any agent framework.
2. **Token-in-Token-Out (TITO)** — Use raw token IDs from vLLM rollouts; never re-tokenize for RL loss computation.
3. **Async RL** — Decouple vLLM inference engine from training loop for 2-3x throughput.
4. **Format-aware regularization** — Penalize malformed tool calls even if the action is logically correct.
5. **60/30/10 data mix** — SWE trajectories / general tool-use / code-as-action by token volume.
## Benchmarks
- **SWE-bench Verified** — Primary real-world software engineering benchmark
- **Terminal-Bench 2.0** — Terminal/agent task completion
- **BFCL v3** — Multi-turn function calling
- **Aider-Polyglot** — Multi-language code editing
- **tau-bench** — Long-horizon multi-turn tool use
## Citation
If you use this recipe, please cite the underlying research:
```bibtex
@article{nemotron-terminal-2026,
title={Nemotron-Terminal: Scalable Training for Terminal-Capable Language Models},
author={NVIDIA},
journal={arXiv:2602.21193},
year={2026}
}
@article{klear-agentforge-2025,
title={Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling},
author={Klear-AI},
journal={arXiv:2511.05951},
year={2025}
}
@article{glm5-2026,
title={GLM-5: from Vibe Coding to Agentic Engineering},
author={Zhipu AI},
journal={arXiv:2602.15763},
year={2026}
}
```
## License
The training guide and scripts are provided as-is for research and educational purposes. Dataset and base model licenses apply to their respective owners.
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