Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable
cs.LG, cs.CL
Submitted: 2026-08-27
Updated: 2026-08-27
Code: https://github.com/openai/grade-school-mathhttps:
Terminology
Sources
- Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
- Distillation Scaling Laws
- Gemma 3 Technical Report
- Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
- You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases
- Measuring Mathematical Problem Solving With the MATH Dataset
- Training Verifiers to Solve Math Word Problems
- Distilling the Knowledge in a Neural Network
- Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation
- Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
- Phantom Transfer: Data Poisoning can Survive Data-Level Defences
- Seed-Induced Uniqueness in Transformer Models: Subspace Alignment Governs Subliminal Transfer
- Sustained Gradient Alignment Mediates Subliminal Learning in a Multi-Step Setting: Evidence from MNIST Auxiliary Logit Distillation Experiment
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- Scaling Laws of Synthetic Data for Language Models
- Qwen2.5 Technical Report
- DoRA: Weight-Decomposed Low-Rank Adaptation
- Qwen3 Technical Report
- The Llama 3 Herd of Models
- Subliminal Signals in Preference Labels
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