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Knowledge Distillation for GamePals LLM

Partial automation is an interaction paradigm in which users delegate part of a system’s control to a software agent.

Prior work has shown its potential to improve accessibility in video games by assisting players with disabilities in real- time control. However, experimental evidence also indicates that the lack of communication between user and software agent can lead to confusion and misunderstandings.

To address this limitation, large language models (LLMs) can interpret natural language voice commands and translate them into executable game actions. Although frontier LLMs are capable of this task, real-time game control imposes strict low-latency requirements, motivating the use of local models.

In this work, we formalize human-commanded game control and distill the knowledge of a frontier teacher model (GPT-5.1) into two smaller local student models (Qwen2.5-1.5B-Instruct and Qwen2.5-0.5B-Instruct), evaluating whether they preserve the teacher’s capabilities while reducing inference latency. Preliminary results show over a 20x reduction in response time while maintaining strong performance on more than 90% of the test set.

Knowledge Distillation Pipeline

The full KD pipeline proposed for the task of human-commanded game control is contained in the 8 notebooks in this repository:

  1. Game State Processing: build a dataset of suitable game states.
  2. User Commands Generation: generate user commands associated with the game states, using the teacher model.
  3. Dataset Preparation: build the dataset of <game state, user command> pairs.
  4. Dataset Labeling: run inference on the teacher model, to get labels for each input pair.
  5. Untrained Student: run inference on the untrained student model, as baseline.
  6. Student Training: train the student on the human-commanded game control task.
  7. Trained Student: re-run inference on the student model, after training.
  8. Model Comparisons: compare results between teacher and student, as well as between different students.

A representation of the KD Pipeline by Gemini:

KD Pipeline

Content of this Repository

This repository has the following structure:

  • 01..08_notebooks contain the KD pipeline, as presented above.
  • data contains all data used and generated throughout the experiments.
  • prompts contains the generic templates for the prompts and filling parts for The Ultimate Doom.
  • src contains utility classes and functions used throughout the notebooks.

Author: Filippo Corti

Natural Language Procesing Project - A.Y. 2025/2026

Useful References: