Regret Pre-training: Bridging Prior and Posterior Views for Enhanced Knowledge Grounding

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Regret Pre-training (RPT) is a novel self-supervised framework designed to enhance knowledge grounding in causal language models by exploiting future information during training. Addressing the limitation that causal LMs typically only use preceding context, RPT employs a dual-view architecture within a single model, generating both a causal Student and a future-conditioned Teacher distribution. The training objective combines standard language modeling with a regret loss, minimizing KL divergence from the Teacher to the Student to transfer future-aware signals. Investigated on the OLMoE-1B-7B architecture, two teacher configurations, LocalRegret and GlobalRegret, were tested. After 4 billion tokens of training, both consistently outperformed the baseline across nine downstream tasks, with GlobalRegret achieving 33.9% accuracy and LocalRegret 32.2%, compared to the baseline's 30.2%. Notably, GlobalRegret boosted BoolQ performance by 18.1 percentage points, from 42.9% to 61.0%. The framework introduces no new parameters and requires only one additional inference-mode forward pass per training step.

Key takeaway

For Machine Learning Engineers developing causal language models, especially for knowledge-intensive tasks like question answering, you should consider integrating Regret Pre-training. This framework significantly improves performance on tasks such as BoolQ by 18.1 percentage points without adding model parameters. You can achieve these gains with only one extra inference-mode forward pass per training step, making it an efficient upgrade for existing OLMoE-1B-7B or similar architectures.

Key insights

Regret Pre-training enhances causal language models by leveraging future context through a teacher-student framework, improving performance without adding parameters.

Principles

• Exploit future context in causal LM training.
• Use a dual-view teacher-student architecture.
• Minimize KL divergence for future-aware signals.

Method

Augment standard language modeling with a regret loss, minimizing KL divergence from a future-conditioned Teacher distribution to a causal Student. Teacher configurations include LocalRegret (one future token) and GlobalRegret (bidirectional context with target masked).

In practice

• Improve BoolQ task performance by 18.1 percentage points.
• Enhance knowledge grounding in causal LMs.
• Integrate without adding model parameters.

Topics

• Regret Pre-training
• Causal Language Models
• Knowledge Grounding
• Self-supervised Learning
• OLMoE-1B-7B
• KL Divergence

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.