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cs.LG2025
Relative-Based Scaling Law for Neural Language Models
Baoqing Yue, Jinyuan Zhou, Zixi Wei +3
Scaling laws aim to accurately predict model performance across different scales. Existing scaling-law studies almost exclusively rely on cross-entropy as the evaluation metric. Ho…
cs.LG2025
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
Qingyao Ai, Yichen Tang, Changyue Wang +3
Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual…
cs.LG2025
What Scales in Cross-Entropy Scaling Law?
Junxi Yan, Zixi Wei, Qingyao Ai +2
The cross-entropy scaling law has long served as a key tool for guiding the development of large language models. It shows that cross-entropy loss decreases in a predictable power-…