Distilling Reasoning: Exploring the Boundaries

Ujjwal Nadhani — Tue, 01 Sep 2026 00:00:00 +0000

I’m currently working towards training a task-specific LLM for chess reasoning: a model that can identify the key ideas in a chess position and explain them clearly in natural language. As a chess learner, my goal is to build something that I can learn from, rather than a model that simply outputs the best move.

A natural first approach was knowledge distillation: using a stronger teacher LLM to transfer broad chess-reasoning ability to a smaller student. This was attractive because distillation can, in principle, recover much of a teacher’s capability with far less training data than conventional methods. To understand how effective and viable this approach might be for chess reasoning, I started with the literature.

Synthetic Data for Chess Explanation: Building Human-Relevant Reasoning Traces

Ujjwal Nadhani — Tue, 01 Sep 2026 00:00:00 +0000

Introduction and Initial Approach

I’m currently working on training an LLM that explains the key ideas in a chess position. Over the years, frontier LLMs have gotten decent at playing moves. However, as a chess learner, I care more about the explanations and rationales behind the moves, which are often wrong: they mislabel pieces on the board, highlight irrelevant ideas, and worst of all, provide no insightful analysis.

Welcome on UN

Distilling Reasoning: Exploring the Boundaries

Synthetic Data for Chess Explanation: Building Human-Relevant Reasoning Traces

Introduction and Initial Approach