arXiv AI
The Shape of the DPO Hyperparameter Landscape: What to Measure, How It Moves with Scale, and When Not to Transfer It
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Efficient Hyperparameter Optimization for LLM Reinforcement Learning
arXiv:2606. 03073v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization (HPO) essential yet computationally expensive.
arXiv Machine Learning
Sep 30
UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?
arXiv:2609.37076v1 Announce Type: new Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this is...
arXiv AI
Sep 30
Trajectory Soup: Pushing the Compute-Scaling Frontier of LLM Mid-training via Diverse Trajectories
arXiv:2609.37169v1 Announce Type: cross Abstract: Mid-training equips pretrained large language models with specialized and reasoning capabilities, but the returns of this stage are bounded since add...
Hugging Face Trending Papers
Sep 1
Post-Training Science for Supervised Fine-Tuning
Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, which optimiser, and what data to feed the model. Eac...
arXiv AI
Jun 11
Soft-Prompt Tuning for Fair and Efficient LLM Benchmark Evaluation
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
arXiv Machine Learning
Jun 2
BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining
arXiv:2510. 06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks.