统一LLM对齐训练流程，让实验可复现且支持多种后端。
AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models
- 模块化设计，支持SFT与强化学习优化的统一接口。
- 兼容TRL和Unsloth后端，实现可复现的对齐实验。
- 内置规则与学习型奖励层，适配标准与自定义任务。
后训练对齐是部署大语言模型的核心环节，但实际工作流仍分散在不同后端工具与临时拼接代码中，导致实验难以复现。我们识别出后端干扰、奖励碎片化和不可复现流水线是主要障碍。提出AlignTune，一个模块化工具包，提供监督微调（SFT）与类RLHF优化的统一接口，支持可互换的TRL和Unsloth后端。它标准化配置，提供可扩展的奖励层（规则与学习型），并集成在标准基准和自定义任务上的评估。通过将后端逻辑封装在单一工厂边界内，AlignTune实现受控对比与可复现的对齐实验。
原文摘要 · Abstract (English)
Post-training alignment is central to deploying large language models (LLMs), yet practical workflows remain split across backend-specific tools and ad-hoc glue code, making experiments hard to reproduce. We identify backend interference, reward fragmentation, and irreproducible pipelines as key obstacles in alignment research. We introduce AlignTune, a modular toolkit exposing a unified interface for supervised fine-tuning (SFT) and RLHF-style optimization with interchangeable TRL and Unsloth backends. AlignTune standardizes configuration, provides an extensible reward layer (rule-based and learned), and integrates evaluation over standard benchmarks and custom tasks. By isolating backend-specific logic behind a single factory boundary, AlignTune enables controlled comparisons and reproducible alignment experiments.
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