mlsanityData to 7 Oct 2026

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

arXiv 2602.09621 ↗First posted 10 Feb 2026cs.CL cs.LGcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 17 of its 30 references that are in the map. First posted on arXiv 10 Feb 2026.

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17 of 30 references.

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  1. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  7. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  8. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  9. 2017Deep reinforcement learning from human preferencescited by 3,846
  10. 2021Program Synthesis with Large Language Modelscited by 3,509
  11. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  12. 2023Mistral 7Bcited by 3,065
4 more
  1. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  2. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,678
  3. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  4. 2026Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoningcited by 2

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  2. 2023Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models
  3. 2025Towards Efficient and Effective Alignment of Large Language Models
  4. 2025A Survey on Training-free Alignment of Large Language Models
  5. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
  6. 2026Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines
  7. 2024DeAL: Decoding-time Alignment for Large Language Models
  8. 2024Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models