arXiv Machine Learning
Aug 11
Early Data Exposure Improves Robustness to Subsequent Fine-Tuning
arXiv:2605. 12705v2 Announce Type: replace Abstract: How can we train models whose post-trained capabilities survive subsequent fine-tuning?
Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT
arXiv:2607. 25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves.
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arXiv Machine Learning
Jul 30
Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models
arXiv:2607. 26173v1 Announce Type: new Abstract: Alignment training, model organisms, and toy models are usually treated as separate research areas.
arXiv Machine Learning
Jul 22
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
arXiv:2607. 18639v1 Announce Type: new Abstract: Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.
arXiv Machine Learning
Jul 30
Constitutional Midtraining: Content Presence Drives Alignment Gains
arXiv:2607. 26654v1 Announce Type: cross Abstract: Post-training alignment is often shallow, eroding under fine-tuning.
arXiv AI
Jul 22
Measuring Reward-Seeking via Contrastive Belief Updates
arXiv:2607. 18966v1 Announce Type: new Abstract: Language models trained with reinforcement learning may learn to optimize the grader's judgment rather than the intended objective.
arXiv AI
Jul 3
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
arXiv:2607. 01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal.
Hugging Face Trending Papers
Jul 21
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e. g.
Hugging Face Trending Papers
Jul 28
Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models
Alignment training, model organisms, and toy models are usually treated as separate research areas. But projects in all three frequently use supervised fine-tuning (SFT) to pursue the same underlying goals.
arXiv AI
Jun 12
Prefill Awareness in Large Language Models
arXiv:2606. 12747v1 Announce Type: new Abstract: Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model outputs.
arXiv Machine Learning
Aug 12
Same Targets, Different Computation: How Post-Training Divides Work Across Model Layers
arXiv:2605. 07284v2 Announce Type: replace Abstract: A late-layer change learned during post-training may work on the base model's earlier state, or it may depend on earlier computation learned with it.
Hugging Face Trending Papers
Jul 13
HyperSafe: Inference-Time Safety Recovery for Fine-Tuned Language Models
Safety alignment in large language models can be fragile under fine-tuning, as even benign task adaptation may increase harmful compliance. Existing defenses mainly follow two directions: they either intervene during or after fine-tuning through retraining or weight modification, which can be costly and may hurt task performance, or they use model-agnostic safety classifiers, which may miss failures specific to a given fine-tuned checkpoint.
arXiv AI
Jul 17
Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models
arXiv:2607. 14552v1 Announce Type: cross Abstract: A standard recipe for distilling the reasoning ability of large language models (LLMs) is to sample chains of thought from the model, keep those that reach the correct final answer, and fine-tune on the survivors.