Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
Teddy Ferdinan, BartÅomiej Koptyra, MikoÅaj Langner +42
While Reasoning Language Models (RLMs) are rapidly emerging as powerful tools for scientific research, their impact is primarily concentrated in "hard science" fields. The slow --…
cs.AI2026
What properties of reasoning supervision are associated with improved downstream model quality?
MikoÅaj Langner, Dzmitry Pihulski, Jan Eliasz +5
Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning datas…
cs.AI2024
Into the Unknown: Self-Learning Large Language Models
Teddy Ferdinan, Jan KocoÅ, PrzemysÅaw Kazienko
We address the main problem of self-learning LLM: the question of what to learn. We propose a self-learning LLM framework that enables an LLM to independently learn previously unkn…