Quiet Feature Learning in Algorithmic Tasks

• URL: http://arxiv.org/abs/2505.03997v1
• Date: Tue, 06 May 2025 22:18:50 GMT
• Title: Quiet Feature Learning in Algorithmic Tasks
• Authors: Prudhviraj Naidu, Zixian Wang, Leon Bergen, Ramamohan Paturi,
• Abstract summary: We train Transformer-based language models on ten foundational algorithmic tasks.<n>We observe pronounced phase transitions in their loss curves that deviate from established power-law scaling trends.
• Score: 1.9249287163937978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We train Transformer-based language models on ten foundational algorithmic tasks and observe pronounced phase transitions in their loss curves that deviate from established power-law scaling trends. Over large ranges of compute, the validation loss barely improves, then abruptly decreases. Probing the models' internal representations reveals the learning of quiet features during the stagnant phase, followed by sudden acquisition of loud features that coincide with the sharp drop in loss. Our ablation experiments show that disrupting a single learned feature can dramatically degrade performance, providing evidence of their causal role in task performance. These findings challenge the prevailing assumption that next-token predictive loss reliably tracks incremental progress; instead, key internal features may be developing below the surface until they coalesce, triggering a rapid performance gain.

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