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Achin Kulshrestha

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2

NeurIPS Conference 2025 Conference Paper

Gatekeeper: Improving Model Cascades Through Confidence Tuning

  • Stephan Rabanser
  • Nathalie Rauschmayr
  • Achin Kulshrestha
  • Petra Poklukar
  • Wittawat Jitkrittum
  • Sean Augenstein
  • Congchao Wang
  • Federico Tombari

Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to offload complex tasks. However, existing approaches inadequately balance the capabilities of these models, often resulting in unnecessary deferrals or sub-optimal resource usage. In this work, we introduce a novel loss function called Gatekeeper for calibrating smaller models in cascade setups. Our approach fine-tunes the smaller model to confidently handle tasks it can perform correctly while deferring complex tasks to the larger model. Moreover, it incorporates a mechanism for managing the trade-off between model performance and deferral accuracy and is broadly applicable across various tasks and domains without any architectural changes. We evaluated our method on encoder-only, decoder-only, and encoder-decoder architectures. Experiments across image classification, language modeling, and vision-language tasks show that our approach substantially improves deferral performance.

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ICML Conference 2025 Conference Paper

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

  • Shangbin Feng
  • Zifeng Wang 0002
  • Yike Wang 0002
  • Sayna Ebrahimi
  • Hamid Palangi
  • Lesly Miculicich
  • Achin Kulshrestha
  • Nathalie Rauschmayr

We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and optimize a utility function representing model adaptation objectives. Compared to existing model composition approaches, Model Swarms offers tuning-free model adaptation, works in low-data regimes with as few as 200 examples, and does not require assumptions about specific experts in the swarm or how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21. 0% across tasks and contexts. Further analysis reveals that LLM experts discover previously unseen capabilities in initial checkpoints and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.

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