The FIL Hypothesis: Inductive Biases Help with Kernel Engineering

2026-06-29

AI Summary

This work addresses the challenge of scaling purely data-driven approaches in real-world AI tasks with long feedback information loops (FILs), such as GPU programming, where sparse validation signals hinder learning. Challenging the prevailing paradigm that general-purpose methods will ultimately prevail, this study introduces FIL duration as a critical scalability dimension and proposes integrating human prior knowledge to inject inductive bias. Specifically, it constrains the solution space, designs kernel functions grounded in domain-specific inductive biases, and models GPU kernel performance to effectively compensate for the limitations of data-driven methods. Experimental results demonstrate that the proposed approach significantly outperforms purely data-driven baselines on realistic GPU programming tasks, underscoring the efficacy and necessity of combining expert knowledge with inductive bias in long-FIL scenarios.

Abstract

The Bitter Lesson, which posits that general-purpose methods that scale with computation and data ultimately outperform those with built-in human knowledge, has become a dominant paradigm in the era of Large Language Models. We revisit this principle by observing a new and critical scaling dimension: the duration of the Feedback Information Loop (FIL), the time required for a system to receive a verification signal after generating a prediction. Most historic successes in Artificial Intelligence (AI) have benefited from near instantaneous feedback (e.g., games or classification tasks), but we argue that future AI applications in science and the physical world will inherently involve FILs ranging from hours to weeks. This trend poses a fundamental scaling limit, as obtaining enough verification steps required by purely data-driven methods becomes practically impossible. Additionally, we propose a method that is orthogonal to purely data-driven approaches, based on human-inspired expert knowledge. The method relies on inductive biases and constraining the solution space. We provide an initial validation of the hypothesis and the method, by studying the real-world GPU programming task, a domain with non-trivial FIL, and demonstrate that incorporating inductive biases yields superior performance over data-driven approaches. The code is released under: https://github.com/ai-nikolai/robust_kernelbench

Problem

• Feedback Information Loop
• inductive biases
• data-driven methods
• scaling limit
• AI in science

Innovation

• Feedback Information Loop
• Inductive Biases
• Kernel Engineering
• Human-inspired Knowledge
• Scaling Limit