Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

R E Zera Marveen Lyngkhoi, Chirag Chawla, Pratinav Seth, Utsav Avaiya, Soham Bhattacharjee, Mykola Khandoga, Rui Yuan, Vinay Kumar Sankarapu

TL;DR

AlignTune tackles fragmentation in post-training alignment workflows by providing a modular, multi-backend toolkit that unifies SFT and RLHF across TRL and Unsloth backends. It introduces a backend factory with environment isolation, a first-class reward modeling pipeline, and an integrated evaluation harness to enable reproducible comparisons. Benchmark results show that backend choice can be swapped without degrading final alignment quality, while Unsloth offers speed/memory gains. The work delivers open-source tooling for auditable, domain-adapted alignment and lowers barriers to rigorous, reproducible research.

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.

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

TL;DR

AlignTune tackles fragmentation in post-training alignment workflows by providing a modular, multi-backend toolkit that unifies SFT and RLHF across TRL and Unsloth backends. It introduces a backend factory with environment isolation, a first-class reward modeling pipeline, and an integrated evaluation harness to enable reproducible comparisons. Benchmark results show that backend choice can be swapped without degrading final alignment quality, while Unsloth offers speed/memory gains. The work delivers open-source tooling for auditable, domain-adapted alignment and lowers barriers to rigorous, reproducible research.

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.
Paper Structure (63 sections, 8 figures, 10 tables)

This paper contains 63 sections, 8 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Installation and Getting Started
  3. Library Structure
  4. Scope and Definitions
  5. High-Level Architecture
  6. Core API and Class Hierarchy
  7. Factory API
  8. Trainer Class Hierarchy
  9. Multi-Backend Architecture
  10. Backend Isolation System
  11. Training Algorithms
  12. Reward System
  13. Reward Class Hierarchy
  14. Reward Function Registry
  15. Reward-Model Training Pipeline
  16. ...and 48 more sections

Figures (8)

  • Figure 1: The current alignment ecosystem forces a choice between reliability and speed, fragmenting workflows and hindering reproducibility.
  • Figure 2: High-level architecture of AlignTune. Users interact via CLI, Python APIs, or YAML configs. The backend factory routes to TRL or Unsloth unsloth2024 backends, which expose SFT and RL trainers. Reward and evaluation systems are shared across backends.
  • Figure 3: Backend isolation flow. TRL runs block Unsloth patches; Unsloth runs clear isolation flags and import lazily.
  • Figure 4: AlignTune reward system: built-in reward categories, weighted composition, and the pipeline from rule-based rewards to neural reward models.
  • Figure 5: Evaluation and monitoring pipeline: data loading, real-time metrics, benchmark evaluation, and sandboxed code execution.
  • ...and 3 more figures