aiqbench analysis
Regret Pre-training introduces a parameter-free self-supervised framework that leverages future context to enhance causal language model performance without increasing model size. By minimizing the divergence between a causal student and a future-conditioned teacher, the method achieves significant accuracy gains across nine downstream tasks, with the GlobalRegret variant notably boosting BoolQ performance by over 18 percentage points. This approach offers a practical pathway for improving knowledge grounding in large language models through efficient training modifications rather than architectural bloat.
Regret Pre-training: Bridging Prior and Posterior Views for Enhanced Knowledge Grounding
arXiv:2606.03080v1 Announce Type: cross Abstract: Causal language models factorize sequence probabilities using only preceding context, leaving future information unexploited during training despite its availability in the training data. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learning Using Privileged Information (LUPI) paradigm. The framework employs a dual-view architecture in which a single model generates both a causal Student distribution and a future-conditioned Teacher distribution. The training objective augments standard language modeling with a regret loss that minimizes the KL divergence from teacher to student, transferring future-aware signals to the causal representations. We investigate two teacher configurations on the OLMoE-1B-7B architecture:LocalRegret, which extends attention by one future token, andGlobalRegret, which conditions on bidirectional context with the target position masked. Experiments on nine downstream tasks following 4 billion tokens of training demonstrate that both configurations consistently outperform the baseline. On average,GlobalRegret andLocalRegret achieve 33.9% and 32.2% accuracy respectively, surpassing the baseline's 30.2%. Most notably,GlobalRegret improves BoolQ performance by 18.1 percentage points (61.0% vs 42.9%). The framework introduces no additional parameters and requires only one extra inference-mode forward pass per training step.
Abstract:Causal language models factorize sequence probabilities using only preceding context, leaving future information unexploited during training despite its availability in the training data. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learning Using Privileged Information (LUPI) paradigm. The framework employs a dual-view architecture in which a single model generates both a causal Student distribution and a future-conditioned Teacher distribution. The training objective augments standard language modeling with a regret loss that minimizes the KL divergence from teacher to student, transferring future-aware signals to the causal representations. We investigate two teacher configurations on the OLMoE-1B-7B architecture:LocalRegret, which extends attention by one future token, andGlobalRegret, which conditions on bidirectional context with the target position masked. Experiments on nine downstream tasks following 4 billion tokens of training demonstrate that both configurations consistently outperform the baseline. On average,GlobalRegret andLocalRegret achieve 33.9% and 32.2% accuracy respectively, surpassing the baseline's 30.2%. Most notably,GlobalRegret improves BoolQ performance by 18.1 percentage points (61.0% vs 42.9%). The framework introduces no additional parameters and requires only one extra inference-mode forward pass per training step.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.03080 [cs.CL]
(or arXiv:2606.03080v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.03080 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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