HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench

arXiv:2601.20255v3 Announce Type: replace Abstract: SWE-bench has emerged as the premier benchmark for evaluating Large Language Models on complex software engineering tasks. While these capabilities are fundamentally acquired during the mid-training phase and subsequently elicited during Supervised Fine-Tuning (SFT), there remains a critical deficit in metrics capable of guiding mid-training effectively. Standard metrics such as Perplexity (PPL) are compromised by the "Long-Context Tax" and exhibit weak correlation with downstream SWE performance. In this paper, we bridge this gap by first in

Source: arXiv cs.LG — read the full report at the original publisher.

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