1.6k citations · 2.2k across the 16 of their papers we have counts for
16 papers
Tuning Qwen2.5-VL to Improve Its Web Interaction Skills
Alexandra Yakovleva, Henrik Pärssinen, Harri Valpola +2
Recent advances in vision-language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independent agents that reason and act…
Edit Knowledge, Not Just Facts via Multi-Step Reasoning over Background Stories
Ya Gao, Kalle Kujanpää, Pekka Marttinen +2
Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a central challenge. Existing knowl…
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization
Minttu Alakuijala, Ya Gao, Georgy Ananov +4
As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience remains a key chal…
Efficient Knowledge Injection in LLMs via Self-Distillation
Kalle Kujanpää, Pekka Marttinen, Harri Valpola +1
In many practical applications, large language models (LLMs) need to acquire new knowledge not present in their pre-training data. Efficiently leveraging this knowledge usually rel…
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund +4
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting ina…
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization
Norman Di Palo, Harri Valpola
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computation…