How Do Large Language Monkeys Get Their Power (Laws)?
Fuente:
arXiv
Saved in:
| Main Authors: | Schaeffer, Rylan, Kazdan, Joshua, Hughes, John, Juravsky, Jordan, Price, Sara, Lynch, Aengus, Jones, Erik, Kirk, Robert, Mirhoseini, Azalia, Koyejo, Sanmi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024)
by: Hughes, John, et al.
Published: (2024)
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)
by: Gupta, Isha, et al.
Published: (2025)
CodeMonkeys: Scaling Test-Time Compute for Software Engineering
by: Ehrlich, Ryan, et al.
Published: (2025)
by: Ehrlich, Ryan, et al.
Published: (2025)
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
by: Brown, Bradley, et al.
Published: (2024)
by: Brown, Bradley, et al.
Published: (2024)
Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
Pretraining Scaling Laws for Generative Evaluations of Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
by: Kazdan, Joshua, et al.
Published: (2024)
by: Kazdan, Joshua, et al.
Published: (2024)
Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulness
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
Scale Dependent Data Duplication
by: Kazdan, Joshua, et al.
Published: (2026)
by: Kazdan, Joshua, et al.
Published: (2026)
Hydragen: High-Throughput LLM Inference with Shared Prefixes
by: Juravsky, Jordan, et al.
Published: (2024)
by: Juravsky, Jordan, et al.
Published: (2024)
What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes
by: Lecomte, Victor, et al.
Published: (2023)
by: Lecomte, Victor, et al.
Published: (2023)
Quantifying the Effect of Test Set Contamination on Generative Evaluations
by: Schaeffer, Rylan, et al.
Published: (2026)
by: Schaeffer, Rylan, et al.
Published: (2026)
The Persistent Vulnerability of Aligned AI Systems
by: Lynch, Aengus
Published: (2026)
by: Lynch, Aengus
Published: (2026)
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)
by: Obbad, Elyas, et al.
Published: (2024)
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
by: Miranda, Brando, et al.
Published: (2023)
by: Miranda, Brando, et al.
Published: (2023)
Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)
by: Jiang, Minhao, et al.
Published: (2024)
On the Role of Temperature Sampling in Test-Time Scaling
by: Wu, Yuheng, et al.
Published: (2025)
by: Wu, Yuheng, et al.
Published: (2025)
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
by: Goldie, Anna, et al.
Published: (2024)
by: Goldie, Anna, et al.
Published: (2024)
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
by: Kwok, Jacky, et al.
Published: (2025)
by: Kwok, Jacky, et al.
Published: (2025)
Quantifying Variance in Evaluation Benchmarks
by: Madaan, Lovish, et al.
Published: (2024)
by: Madaan, Lovish, et al.
Published: (2024)
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Federation of Experts: Communication Efficient Distributed Inference for Large Language Models
by: Abdurrahman, Muhammad Shahir, et al.
Published: (2026)
by: Abdurrahman, Muhammad Shahir, et al.
Published: (2026)
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
by: Belakaria, Syrine, et al.
Published: (2025)
by: Belakaria, Syrine, et al.
Published: (2025)
Discovering Implicit Large Language Model Alignment Objectives
by: Chen, Edward, et al.
Published: (2026)
by: Chen, Edward, et al.
Published: (2026)
Causally Inspired Regularization Enables Domain General Representations
by: Salaudeen, Olawale, et al.
Published: (2024)
by: Salaudeen, Olawale, et al.
Published: (2024)
Let's Measure Information Step-by-Step: AI-Based Evaluation Beyond Vibes
by: Robertson, Zachary, et al.
Published: (2025)
by: Robertson, Zachary, et al.
Published: (2025)
CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs
by: Vo, Truong, et al.
Published: (2025)
by: Vo, Truong, et al.
Published: (2025)
Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models
by: Costello, Caia, et al.
Published: (2025)
by: Costello, Caia, et al.
Published: (2025)
KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
by: Mo, Belinda, et al.
Published: (2025)
by: Mo, Belinda, et al.
Published: (2025)
Why Do Safety Guardrails Degrade Across Languages?
by: Zhang, Max, et al.
Published: (2026)
by: Zhang, Max, et al.
Published: (2026)
Scaling Laws for Downstream Task Performance of Large Language Models
by: Isik, Berivan, et al.
Published: (2024)
by: Isik, Berivan, et al.
Published: (2024)
CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models
by: Lee, Donghyun, et al.
Published: (2024)
by: Lee, Donghyun, et al.
Published: (2024)
TRACE: Capability-Targeted Agentic Training
by: Kang, Hangoo, et al.
Published: (2026)
by: Kang, Hangoo, et al.
Published: (2026)
Similar Items
-
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025) -
Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024) -
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025) -
CodeMonkeys: Scaling Test-Time Compute for Software Engineering
by: Ehrlich, Ryan, et al.
Published: (2025) -
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
by: Brown, Bradley, et al.
Published: (2024)