AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models

2026-02-10

AI Summary

This work addresses the fragmentation, irreproducibility, and backend interference commonly observed in current post-training alignment pipelines for large language models. To mitigate these issues, the authors propose a modular toolkit that abstracts diverse backend implementations—such as TRL and Unsloth—through a unified interface. The framework employs a factory pattern to encapsulate both supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) workflows, and introduces an extensible reward layer capable of integrating rule-based and learned reward signals. By supporting standardized configuration, plug-and-play backends, and a comprehensive evaluation mechanism, the proposed system significantly enhances the reproducibility, controllability, and cross-task comparability of alignment experiments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• post-training alignment
• reproducibility
• backend interference
• reward fragmentation
• LLM alignment

Innovation

• modular toolkit
• post-training alignment
• unified interface
• reproducible pipelines
• extensible reward layer