LaCy: What Small Language Models Can and Should Learn is Not Just a Question of Loss
Apple Machine Learning Journal / 4/9/2026
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Key Points
- The article presents the paper "LaCy: What Small Language Models Can and Should Learn is Not Just a Question of Loss," which argues that deciding what smaller language models should learn involves more than optimizing the loss function alone.
- It situates the work within ICLR-related research (April 2026) and frames the contribution as guidance for training or selecting learning objectives/behaviors for small LMs.
- The authorship and publication metadata link the study to the Methods and Algorithms research area, indicating a methodological focus on learning dynamics rather than deployment tooling.
- By emphasizing “what and should learn,” the paper implicitly encourages practitioners to rethink training design choices beyond standard likelihood-based objectives.
This paper was accepted at the Workshop on Memory for LLM-Based Agentic Systems at ICLR.
Language models have consistently grown to compress more world knowledge into their parameters, but the knowledge that can be pretrained into them is upper-bounded by their parameter size. Especially the capacity of Small Language Models (SLMs) is limited, leading to factually incorrect generations. This problem is often mitigated by giving the SLM access to an outside source: the ability to query a larger model, documents, or a database. Under this setting, we study the fundamental question of which…
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