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LLM Pretraining & Post-training
How large language models are built: data curation and mixtures, pretraining objectives, scaling laws, instruction tuning, preference learning, and the full post-training stack.
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AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
Agents · Sep 8
0
Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
LLM Training · Sep 8 · 7:52
0
NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
Agents · Sep 8
0
Revisiting Complete Reasoning Traces for Post-Training
LLM Training · Sep 7
0
Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training
Inference Optimization · Sep 7
0
Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning
Evaluation · Sep 6
0
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
Inference Optimization · Sep 4
0
Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models
Alignment · Sep 4
0
RISE: Recursive Improvement via Self-Extrapolating Policy Distillation
LLM Training · Sep 4
0
When Models Edit Too Much: On the Fidelity of Minimal Code Edits
Code Generation · Sep 3
0