23 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…
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions
Weizhe Yuan, Jane Yu, Song Jiang +8
Scaling reasoning capabilities beyond traditional domains such as math and coding is hindered by the lack of diverse and high-quality questions. To overcome this limitation, we int…
Training Large Language Models to Reason in a Continuous Latent Space
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su +4
Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-thought (CoT) to solve complex p…
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…
RESTRAIN: From Spurious Votes to Signals -- Self-Driven RL with Self-Penalization
Zhaoning Yu, Will Su, Leitian Tao +9
Reinforcement learning with human-annotated data has boosted chain-of-thought reasoning in large reasoning models, but these gains come at high costs in labeled data while falterin…
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
Alisia Lupidi, Carlos Gemmell, Nicola Cancedda +5
Synthetic data generation has recently emerged as a promising approach for enhancing the capabilities of large language models (LLMs) without the need for expensive human annotatio…