Cover Image

From Simulation to Enaction: Post-trained language models recognize and react to their own generations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	G., Asvin, Lindsey, Jack
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Post-training makes large language models less human-like
by: Binz, Marcel, et al.
Published: (2026)

Stylometry recognizes human and LLM-generated texts in short samples
by: Przystalski, Karol, et al.
Published: (2025)

Lightweight reranking for language model generations
by: Jain, Siddhartha, et al.
Published: (2023)

Diffusion on language model encodings for protein sequence generation
by: Meshchaninov, Viacheslav, et al.
Published: (2024)

The Impact of Post-training on Data Contamination
by: Kocyigit, Muhammed Yusuf, et al.
Published: (2026)

Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation
by: Burns, Thomas F, et al.
Published: (2025)

Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
by: Li, Hao, et al.
Published: (2026)

Data filtering methods for training language models
by: Shevchenko, Egor, et al.
Published: (2026)

Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
by: MacKnight, Robert, et al.
Published: (2025)

Prior-informed optimization of treatment recommendation via bandit algorithms trained on large language model-processed historical records
by: Nessari, Saman, et al.
Published: (2025)

Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language models
by: Sowka, Kacper, et al.
Published: (2024)

Improving training time and GPU utilization in geo-distributed language model training
by: Palak, et al.
Published: (2024)

Aviary: training language agents on challenging scientific tasks
by: Narayanan, Siddharth, et al.
Published: (2024)

Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena
by: Luo, Haipeng, et al.
Published: (2024)

AI-AI Bias: large language models favor communications generated by large language models
by: Laurito, Walter, et al.
Published: (2024)

Auditing language models for hidden objectives
by: Marks, Samuel, et al.
Published: (2025)

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
by: Motwani, Sumeet Ramesh, et al.
Published: (2026)

BoA: Attention-aware Post-training Quantization without Backpropagation
by: Kim, Junhan, et al.
Published: (2024)

It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt
by: Bladon, Stuart, et al.
Published: (2026)

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss
by: de Frutos, José Manuel, et al.
Published: (2024)

Prioritized Replay for RL Post-training
by: Fatemi, Mehdi
Published: (2026)

The promising potential of vision language models for the generation of textual weather forecasts
by: Steele, Edward C. C., et al.
Published: (2025)

Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them
by: Jin, Jiahe, et al.
Published: (2025)

AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training
by: Zhang, Huishuai, et al.
Published: (2025)

Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
by: Nielsen, Jacob, et al.
Published: (2025)

Effective internal language model training and fusion for factorized transducer model
by: Guo, Jinxi, et al.
Published: (2024)

What happens when generative AI models train recursively on each others' outputs?
by: Vu, Hung Anh, et al.
Published: (2025)

How predictable is language model benchmark performance?
by: Owen, David
Published: (2024)

Alignment faking in large language models
by: Greenblatt, Ryan, et al.
Published: (2024)

Automatic Pair Construction for Contrastive Post-training
by: Xu, Canwen, et al.
Published: (2023)

Early-stopping for Transformer model training
by: He, Jing, et al.
Published: (2025)

Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks
by: Mei, Taiyuan, et al.
Published: (2024)

Quantifying construct validity in large language model evaluations
by: Kearns, Ryan Othniel
Published: (2026)

Applying sparse autoencoders to unlearn knowledge in language models
by: Farrell, Eoin, et al.
Published: (2024)

FoldToken2: Learning compact, invariant and generative protein structure language
by: Gao, Zhangyang, et al.
Published: (2024)

PBP: Post-training Backdoor Purification for Malware Classifiers
by: Nguyen, Dung Thuy, et al.
Published: (2024)

Post-training for Efficient Communication via Convention Formation
by: Hua, Yilun, et al.
Published: (2025)

Efficient Post-training Quantization with FP8 Formats
by: Shen, Haihao, et al.
Published: (2023)

Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction
by: Văduva, Alin-Gabriel, et al.
Published: (2026)

Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training
by: Barakat, Anas, et al.
Published: (2026)