Researchers have released multiple approaches to address efficiency challenges in large language models, spanning training optimization, inference acceleration, and environmental impact.
According to a paper on arxiv.org, Compute Aligned Training proposes aligning training objectives with test-time strategies to improve LLM performance. The authors state that standard post-training methods “optimize the likelihood of individual samples under a base policy, creating a misalignment with test time procedures that rely on aggregated or filtered outputs.” Their method derives new loss functions for Supervised Fine-Tuning and Reinforcement Learning that maximize performance when test-time strategies are applied, with empirical evidence showing “substantial” improvements in test time scaling.
Another arxiv.org paper introduces TopK-Guided, a training-free method for activation sparsity that speeds up inference by setting unimportant activations to zero. According to the researchers, TopK-Guided “consistently improves perplexity and downstream accuracy” over existing methods TEAL and WINA across Llama-2 and Llama-3 models while preserving similar compute requirements.
Addressing environmental concerns, a third arxiv.org paper analyzing 5,285 papers from NeurIPS 2025 found that “reporting of environmental impact is nearly non-existent.” The authors propose standardized sustainability metrics and introduce the “Smallest Model that Achieves the Job” framework to prioritize computational efficiency alongside accuracy.
Finally, arxiv.org reports on JevSpawn, which addresses the slowness of LLM agents that “generate intermediate reasoning and actions token by token.” The system uses compositional action spaces and parallel action spawning to reduce repeated generation and context computation.