AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models
This addresses a practical issue for researchers and practitioners in AI alignment by offering a tool to improve reproducibility, but it is incremental as it builds on existing methods without new algorithmic breakthroughs.
The paper tackles the problem of fragmented and irreproducible workflows in post-training alignment of large language models by introducing AlignTune, a modular toolkit that standardizes configuration and enables controlled comparisons, though no concrete performance numbers are provided.
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.