arXiv:2606.26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold detailed data construction, filtering rules and training recipes, which hinders community reproducibility and lightweight model optimization. This work presents NebulaExp, a fully transparent, ablation-driven post-training pipeline built on Qwen3-8B-base, covering two orthogonal model branches: general instruct model and complex reasoning-specialized model. We curate a raw corpus of 3.84M multi-so
The proliferation of various large language models and the increasing demand for optimized, specialized AI applications necessitate more rigorous and transparent post-training methodologies.
This work directly addresses the reproducibility and optimization challenges in LLM development, offering a transparent pathway to enhance model performance and facilitate lightweight deployment, which is crucial for broader AI adoption.
The transparency and detailed methodology proposed for post-training pipelines could standardize development practices, making advanced LLM alignment more accessible and efficient for researchers and developers.
- · AI researchers and developers
- · Organizations developing specialized AI applications
- · Open-source AI community
- · Proprietary black-box AI model developers
- · Organizations without robust alignment pipelines
NebulaExp will enable more reproducible and optimized LLMs, fostering faster innovation in specific application domains.
Improved efficiency in post-training could lead to a proliferation of highly specialized and performant AI models, accelerating the adoption of AI agents.
The transparency provided could democratize access to advanced LLM optimization techniques, potentially reducing the development gap between large and small AI entities.
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Read at arXiv cs.AI