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Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe
Soumye Singhal, Joseph Jennings, Ka Shun Shum +11
Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stag…
LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning
Mengmeng Ji, Ravi Shanker Raju, Jonathan Lingjie Li +1
As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a critical bottleneck. Context…
Training Domain Draft Models for Speculative Decoding: Best Practices and Insights
Fenglu Hong, Ravi Raju, Jonathan Lingjie Li +5
Speculative decoding is an effective method for accelerating inference of large language models (LLMs) by employing a small draft model to predict the output of a target model. How…
LLMs Know What to Drop: Self-Attention Guided KV Cache Eviction for Efficient Long-Context Inference
Guangtao Wang, Shubhangi Upasani, Chen Wu +5
Efficient long-context inference is critical as large language models (LLMs) adopt context windows of ranging from 128K to 1M tokens. However, the growing key-value (KV) cache and…
SambaLingo: Teaching Large Language Models New Languages
Zoltan Csaki, Bo Li, Jonathan Li +7
Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues h…