From the 1 of 4 linked papers with an AI index.
4 papers
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Jens Tuyls, Dylan J. Foster, Akshay Krishnamurthy +1
The paper introduces a simple representation‑based exploration bonus derived from a language model's hidden states, which improves the diversity of generated behaviors and boosts p…
The Coverage Principle: How Pre-Training Enables Post-Training
Fan Chen, Audrey Huang, Noah Golowich +5
Language models demonstrate remarkable abilities when pre-trained on large text corpora and fine-tuned for specific tasks, but how and why pre-training shapes the success of the fi…
On the Query Complexity of Verifier-Assisted Language Generation
Edoardo Botta, Yuchen Li, Aashay Mehta +3
Recently, a plethora of works have proposed inference-time algorithms (e.g. best-of-n), which incorporate verifiers to assist the generation process. Their quality-efficiency trade…
Self-Improvement in Language Models: The Sharpening Mechanism
Audrey Huang, Adam Block, Dylan J. Foster +5
Recent work in language modeling has raised the possibility of self-improvement, where a language models evaluates and refines its own generations to achieve higher performance wit…