Semi-Offline Reinforcement Learning for Optimized Text Generation

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

A novel "semi-offline reinforcement learning" (RL) paradigm is introduced for optimizing text generation, bridging the gap between traditional online and offline RL settings. This approach balances exploration capabilities with training costs, offering a theoretical framework for comparing different RL configurations. The proposed semi-offline RL setting, specifically implemented with masked observations, is proven optimal across three properties: minimum optimization cost (requiring only 1 forward propagation per input), minimum asymptotic bias, and minimum overfitting error bound. Extensive experiments on diverse text generation tasks, including summarization (CNN/DM, SAMSum, XSum) and question generation (SQuAD), using models like BART-large, T5-large, and Pegasus-large, demonstrate that this method achieves performance comparable to or superior to state-of-the-art techniques while significantly improving efficiency. The associated code is publicly available.

Key takeaway

For Machine Learning Engineers optimizing large language models for text generation, consider adopting the semi-offline RL approach with masked observations. This method significantly reduces optimization costs to just 1 FP per input while maintaining or improving performance over traditional online or offline RL. You should integrate this technique to achieve efficient exploration and faster training, especially when working with resource-intensive models or large datasets. Experiment with different mask rates and static dataset qualities to fine-tune performance for your specific task.

Key insights

The semi-offline RL paradigm optimizes text generation by blending online exploration and offline efficiency using masked observations.

Principles

• Semi-offline RL balances exploration capability and training cost.
• Optimal RL requires 1 FP, minimal asymptotic bias, and low overfitting error bound.
• Masked observations enable efficient token-wise reward learning for text generation.

Method

Semi-offline RL composes samples by mixing language model-generated tokens and static dataset tokens with a probability p_m ∈ [0,1]. This is implemented using masked observations, where [M] replaces generated tokens.

In practice

• Utilize masked observations for efficient RL fine-tuning of language models.
• Combine MLE loss with RL loss to prevent policy drift during training.
• Consider using lower-quality static datasets ("data-") as they can yield better optimization signals.

Topics

• Reinforcement Learning
• Text Generation
• Semi-Offline RL
• Masked Language Models
• Large Language Models
• Optimization Cost

Code references

• ChangyuChen347/semi-offline-RL

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.