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ARXIV:2606.03080 · LLM TRAINING · SUBMITTED 03 JUN · 20:33 UTC · FRESHNESS FRESH
ARXIV:2606.03080LLM TRAININGSUBMITTED 03 JUN · 20:33 UTCFRESHNESS FRESHMingkuan Zhao · Xiayu Sun · Wentao Hu · Suquan Chen · Jiaxuan Li · Xiaoyan Zhu · +2 at arXiv
A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters.
Opportunity summary
Pain A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence unverified
A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learning…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experiments on nine downstream tasks following 4 billion tokens of training demonstrate that both configurations consistently outperform the baseline. A public repository is linked,…
LLM Training moved forward this cycle; last verified June 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
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Score4.0Analysis summary
A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters.
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Paper Pack
10.48550/arXiv.2606.03080A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters.
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.
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Proof status
unverified0 refs; 4 sources; 83% coverage.
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PROBLEM
A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learn...
METHOD
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 Lear...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experiments on nine downstream tasks following 4 billion tokens of training demonstrate that both configurations consistently outperform the baseline. A public repository is linked, so build verification...
WHY NOW
LLM Training moved forward this cycle; last verified June 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 10, "author": "Mingkuan Zhao; Xiayu Sun; Wentao Hu; Suquan Chen; Jiaxuan Li; Xiaoyan Zhu; Xin Lai; Jiayin Wang"
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A novel pre-training framework enhances causal language models by incorporating future information, leading to significant improvements on downstream tasks without additional parameters.
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4.0/10 public viability
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