Cites: Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

13 citation edges inside this catalog. A title match is weaker evidence than an identifier match. Zero entries does not mean no citations on the web.

• Universal Transformers
  arxiv:1807.03819 · evidence: id
• Scaling Laws for Neural Language Models
  arxiv:2001.08361 · evidence: id
• GLU Variants Improve Transformer
  arxiv:2002.05202 · evidence: id
• Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
  arxiv:2006.16236 · evidence: title
• Memorizing Transformers
  arxiv:2203.08913 · evidence: title
• Cramming: Training a Language Model on a Single GPU in One Day
  arxiv:2212.14034 · evidence: title
• Progress measures for grokking via mechanistic interpretability
  arxiv:2301.05217 · evidence: title
• Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
  arxiv:2309.14316 · evidence: title
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
  arxiv:2402.03300 · evidence: id
• From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
  arxiv:2405.14838 · evidence: id
• Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
  arxiv:2405.18392 · evidence: title
• Training Large Language Models to Reason in a Continuous Latent Space
  arxiv:2412.06769 · evidence: id
• 2 OLMo 2 Furious
  arxiv:2501.00656 · evidence: id