Cited by: The Llama 3 Herd of Models

66 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.

• Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
  arxiv:2405.18392 · evidence: id
• The AdEMAMix Optimizer: Better, Faster, Older
  arxiv:2409.03137 · evidence: title
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
  arxiv:2410.04444 · evidence: id
• Emergent properties with repeated examples
  arxiv:2410.07041 · evidence: id
• Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics
  arxiv:2410.21272 · evidence: id
• Training Large Language Models to Reason in a Continuous Latent Space
  arxiv:2412.06769 · evidence: id
• Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
  arxiv:2412.13663 · evidence: id
• Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
  arxiv:2412.14135 · evidence: title
• 2 OLMo 2 Furious
  arxiv:2501.00656 · evidence: id
• rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
  arxiv:2501.04519 · evidence: id
• Communicating Activations Between Language Model Agents
  arxiv:2501.14082 · evidence: id
• Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming
  arxiv:2501.18837 · evidence: id
• s1: Simple test-time scaling
  arxiv:2501.19393 · evidence: id
• Language Models Use Trigonometry to Do Addition
  arxiv:2502.00873 · evidence: id
• LIMO: Less is More for Reasoning
  arxiv:2502.03387 · evidence: id
• Detecting Strategic Deception Using Linear Probes
  arxiv:2502.03407 · evidence: id
• Large Language Diffusion Models
  arxiv:2502.09992 · evidence: id
• The Lookahead Limitation: Why Multi-Operand Addition is Hard for LLMs
  arxiv:2502.19981 · evidence: id
• The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
  arxiv:2503.02875 · evidence: id
• 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
  arxiv:2503.14858 · evidence: id
• Understanding R1-Zero-Like Training: A Critical Perspective
  arxiv:2503.20783 · evidence: id
• Encoder-Decoder Gemma: Improving the Quality-Efficiency Trade-Off via Adaptation
  arxiv:2504.06225 · evidence: id
• Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
  arxiv:2504.13837 · evidence: id
• TTRL: Test-Time Reinforcement Learning
  arxiv:2504.16084 · evidence: id