Reader: Model-based language-instructed reinforcement learning. - Nicola Dainese; Pekka Marttinen; Alexander Ilin (EMNLP, 2023)

In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery. - Matteo Merler; Katsiaryna Haitsiukevich; Nicola Dainese; Pekka Marttinen (ACL (Student Research Workshop), 2024)

Using Program Repair as a Proxy for Language Models' Feedback Ability in Programming Education. - Charles Koutcheme; Nicola Dainese; Arto Hellas (BEA, 2024)

Can docstring reformulation with an LLM improve code generation? - Nicola Dainese; Alexander Ilin; Pekka Marttinen (EACL (Student Research Workshop), 2024)

Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge. - Charles Koutcheme; Nicola Dainese; Sami Sarsa; Arto Hellas; Juho Leinonen 001; Paul Denny 001 (ITiCSE (1), 2024)

Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search. - Nicola Dainese; Matteo Merler; Minttu Alakuijala; Pekka Marttinen (NeurIPS, 2024)

In-Context Symbolic Regression: Leveraging Language Models for Function Discovery. - Matteo Merler; Nicola Dainese; Katsiaryna Haitsiukevich (CoRR, 2024)

Benchmarking Educational Program Repair. - Charles Koutcheme; Nicola Dainese; Sami Sarsa; Juho Leinonen 001; Arto Hellas; Paul Denny 001 (CoRR, 2024)

Evaluating Language Models for Generating and Judging Programming Feedback. - Charles Koutcheme; Nicola Dainese; Sami Sarsa; Arto Hellas; Juho Leinonen 001; Syed Ashraf; Paul Denny 001 (SIGCSE (1), 2025)

ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models. - Matteo Merler; Nicola Dainese; Minttu Alakuijala; Giovanni Bonetta; Pietro Ferrazzi; Yu Tian; Bernardo Magnini; Pekka Marttinen (CoRR, 2025)