Reposted by Audrey Huang
Announcing the first workshop on Foundations of Language Model Reasoning (FoRLM) at NeurIPS 2025!
📝Soliciting abstracts that advance foundational understanding of reasoning in language models, from theoretical analyses to rigorous empirical studies.
📆 Deadline: Sept 3, 2025
📝Soliciting abstracts that advance foundational understanding of reasoning in language models, from theoretical analyses to rigorous empirical studies.
📆 Deadline: Sept 3, 2025
August 11, 2025 at 3:40 PM
Announcing the first workshop on Foundations of Language Model Reasoning (FoRLM) at NeurIPS 2025!
📝Soliciting abstracts that advance foundational understanding of reasoning in language models, from theoretical analyses to rigorous empirical studies.
📆 Deadline: Sept 3, 2025
📝Soliciting abstracts that advance foundational understanding of reasoning in language models, from theoretical analyses to rigorous empirical studies.
📆 Deadline: Sept 3, 2025
Reposted by Audrey Huang
Given a high-quality verifier, language model accuracy can be improved by scaling inference-time compute (e.g., w/ repeated sampling). When can we expect similar gains without an external verifier?
New paper: Self-Improvement in Language Models: The Sharpening Mechanism
arxiv.org/abs/2412.01951
New paper: Self-Improvement in Language Models: The Sharpening Mechanism
arxiv.org/abs/2412.01951
December 14, 2024 at 4:10 PM
Given a high-quality verifier, language model accuracy can be improved by scaling inference-time compute (e.g., w/ repeated sampling). When can we expect similar gains without an external verifier?
New paper: Self-Improvement in Language Models: The Sharpening Mechanism
arxiv.org/abs/2412.01951
New paper: Self-Improvement in Language Models: The Sharpening Mechanism
arxiv.org/abs/2412.01951