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
The paper addresses a critical need for effective metrics in the mid-training phase of Large Language Models specifically for software engineering tasks, a domain where current evaluation methods are proving inadequate.
Improved mid-training guidance for LLMs in software engineering can significantly accelerate development cycles and enhance the performance of AI agents, leading to more robust and autonomous systems.
The ability to more effectively guide LLM mid-training for software engineering tasks allows for greater efficiency in model development and potentially unlocks new levels of autonomous software creation.
- · AI model developers
- · Software engineering companies
- · AI Agents sector
- · DevOps tooling providers
- · Companies reliant on manual software development
- · Less efficient AI training methodologies
More capable and efficient 'coding-AI' models emerge as mid-training optimization improves.
Accelerated development of AI agents capable of autonomous software creation and improvement.
The role of human software engineers shifts significantly towards oversight and high-level architecture, rather than routine coding.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG