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  Efficient architectures rethink how LLMs should be built, not just shrunk. As scaling saturates and costs explode, the question is no longer “How do we train bigger models?” but rather “How do we design models that use capacity intelligently?” Our work shows that architectural efficiency emerges from preserving geometry, reducing redundancy, aligning reasoning paths, and enabling predictable downscaling.

Efficient Architectures

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  Efficient fine-tuning asks a simple question: How do we adapt large models without paying large-model costs? As LLMs scale, full-model training becomes impractical, and even standard PEFT methods like LoRA can be brittle, sensitive to hyperparameters, or misaligned with downstream reasoning demands.

Efficient Fine-Tuning

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  Efficient inference is where theory meets reality: every token generated has a cost, and at production scale those costs dominate everything else. Instead of making models smaller, efficient inference focuses on making them smarter at runtime—propagating information cleanly, thinking with fewer steps, and using compute only where it matters.

Efficient Inference

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  Knowledge distillation is a central technique in efficient model development, enabling researchers to transfer the capabilities of large, high-performance models into smaller, more deployable ones. At its core, distillation frames learning as a teacher–student paradigm: a powerful teacher model generates softened probability distributions, intermediate representations, or reasoning traces that guide a compact student model during training

Knowledge Distillation

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  Model compression encompasses pruning, quantization, distillation, and related
  techniques to reduce the computational and memory footprint of large models
  while preserving performance.

Model Compression

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  Model coordination asks a simple but transformative question: what if intelligence emerges not from one giant model, but from many smaller ones working together? As LLM ecosystems move toward modular, multi-agent, and expert-driven workflows, coordination becomes the glue that turns collections of models into coherent systems.

Model Coordination

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  Scaling laws give us a scientific compass for navigating model design. Instead of guessing how big a model should be—or how much data it needs—we rely on empirical laws that relate parameters, data, compute, and performance through predictable power-law trends. These laws reveal where scaling helps, where it saturates, and where we are wasting compute.

Scaling Intelligence, Cutting Cost.

Sponsors

Research Areas

Efficient Architectures

Efficient Fine-Tuning

Efficient Inference

Knowledge Distillation

Model Compression

Model Coordination

Efficient Scaling Laws

Recent Publications

The Art of Scaling Test-Time Compute for Large Language Models

Value-Guided KV Compression for LLMs via Approximated CUR Decomposition

On the Generalization vs Fidelity Paradox in Knowledge Distillation

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