1 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
Self-Improving Pretraining: using post-trained models to pretrain better models
Ellen Xiaoqing Tan, Jack Lanchantin, Shehzaad Dhuliawala +9
Large language models are classically trained in stages: pretraining on raw text followed by post-training for instruction following and reasoning. However, this separation creates…
SPICE: Self-Play In Corpus Environments Improves Reasoning
Bo Liu, Chuanyang Jin, Seungone Kim +7
Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where…
Bridging Offline and Online Reinforcement Learning for LLMs
Jack Lanchantin, Angelica Chen, Janice Lan +9
We investigate the effectiveness of reinforcement learning methods for finetuning large language models when transitioning from offline to semi-online to fully online regimes for b…
Multi-Token Attention
Olga Golovneva, Tianlu Wang, Jason Weston +1
Soft attention is a critical mechanism powering LLMs to locate relevant parts within a given context. However, individual attention weights are determined by the similarity of only…
R.I.P.: Better Models by Survival of the Fittest Prompts
Ping Yu, Weizhe Yuan, Olga Golovneva +4
Training data quality is one of the most important drivers of final model quality. In this work, we introduce a method for evaluating data integrity based on the assumption that lo…
Diverse Preference Optimization
Jack Lanchantin, Angelica Chen, Shehzaad Dhuliawala +4
Post-training of language models, either through reinforcement learning, preference optimization or supervised finetuning, tends to sharpen the output probability distribution and…