Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Senyu Li, Zipeng Sun, Jiayi Wang, Xue Liu, Pontus Stenetorp, Siva Reddy, David Ifeoluwa Adelani

TL;DR

This paper introduces Warmup Generations, a task-agnostic framework that learns intermediate warmup sequences to guide sequence-to-sequence generation without external annotations. By viewing warmup generation and finalOutput generation under a reward-based, RL-inspired objective, the method optimizes $c_{\text{init}}$ to maximize $P(y_{\text{target}}|x)$, approximated via Monte Carlo sampling. Experiments across translation, summarization, and logical reasoning demonstrate consistent improvements for both encoder-decoder and decoder-only models, with gains robust to model size and tasks. The approach emphasizes lexical alignment improvements in summarization, offers a simple, low-overhead implementation, and highlights avenues for efficiency optimizations and deeper reasoning enhancements in future work.

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.

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

TL;DR

This paper introduces Warmup Generations, a task-agnostic framework that learns intermediate warmup sequences to guide sequence-to-sequence generation without external annotations. By viewing warmup generation and finalOutput generation under a reward-based, RL-inspired objective, the method optimizes to maximize , approximated via Monte Carlo sampling. Experiments across translation, summarization, and logical reasoning demonstrate consistent improvements for both encoder-decoder and decoder-only models, with gains robust to model size and tasks. The approach emphasizes lexical alignment improvements in summarization, offers a simple, low-overhead implementation, and highlights avenues for efficiency optimizations and deeper reasoning enhancements in future work.

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.

Paper Structure

This paper contains 26 sections, 12 equations, 3 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Human readable intermediate steps
  4. Structured intermediate steps
  5. Reinforcement learning in NLP
  6. Formulation and Derivation
  7. Warmup Generations Approach
  8. Implementation for Encoder-Decoder Models
  9. Implementation for Decoder-Only Models
  10. Rationale Behind Using a Separator Between $c_{init}$ and $y_{target}$
  11. Experiments
  12. Tasks and Datasets
  13. Models
  14. Metrics
  15. Experimental Settings
  16. ...and 11 more sections

Figures (3)

  • Figure 1: High-level workflow of Warmup Generations. The input is first used to generate an intermediate "warmup" sequence, which acts as a guiding context to improve the generation of the final target output for sequence-to-sequence tasks.
  • Figure 2: Comparison of the traditional supervised fine-tuning methods (left) and our proposed method (right). The traditional method directly optimizes the mapping from input to output using annotated data, while our method dynamically generates and optimizes warmup sequences to guide the final output.
  • Figure 3: Models' training loss after each epoch of fine-tuning. The left figure represents T5-base on the LogiQA2 dataset, while the right graph represents mT5-base on the WMT19 dataset for the de-en language pair.