Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation

TL;DR

This paper proposes a task-agnostic method that generates warmup sequences to improve sequence-to-sequence learning, enhancing performance without relying on external supervision or predefined intermediate formats.

Contribution

Introduces a novel, scalable framework for unsupervised warmup sequence generation that guides sequence-to-sequence models across diverse tasks.

Findings

Outperforms traditional supervised fine-tuning methods.

Effective across translation, summarization, and reasoning tasks.

Enhances model performance without external annotations.

Abstract

Traditional supervised fine-tuning (SFT) strategies for sequence-to-sequence tasks often train models to directly generate the target output. Recent work has shown that guiding models with intermediate steps, such as keywords, outlines, or reasoning chains, can significantly improve performance, coherence, and interpretability. However, these methods often depend on predefined intermediate formats and annotated data, limiting their scalability and generalizability. In this work, we introduce a task-agnostic framework that enables models to generate intermediate "warmup" sequences. These warmup sequences, serving as an initial state for subsequent generation, are optimized to enhance the probability of generating the target sequence without relying on external supervision or human-designed structures. Drawing inspiration from reinforcement learning principles, our method iteratively refines these intermediate steps to maximize their contribution to the final output, similar to reward-driven optimization in reinforcement learning with human feedback. Experimental results across tasks such as translation, summarization, and multi-choice question answering for logical reasoning show that our approach outperforms traditional SFT methods, and offers a scalable and flexible solution for sequence-to-sequence tasks.