6 papers · 1 filter
Mind the Delay: Latency-aware Query Routing for Heterogeneous LLM pools
Shivam Patel, Akaash R. Parthasarathy, Ankur Mallick +2
Large language model (LLM) query routers typically assign each query to a suitable model that balances response accuracy and monetary cost. Subsequently, separate system-level load…
Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer
Baris Askin, Muhammed Ustaomeroglu, Anupam Nayak +3
Fine-tuning LLMs on narrow harmful datasets can induce Emergent Misalignment (EM), where models exhibit misaligned behavior far beyond the fine-tuning distribution. We argue that e…
Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents
Neharika Jali, Anupam Nayak, Gauri Joshi
As LLM reasoning performance plateaus, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries. Prior appro…
Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations
Baris Askin, Shivam Patel, Anupam Nayak +4
Large language models (LLMs) are increasingly accessed as remotely hosted services by edge and enterprise clients that cannot run frontier models locally. Since models vary widely…
LOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison, and Selection
Shivam Patel, William Cocke, Gauri Joshi
The rapidly growing ecosystem of Large Language Models (LLMs) makes it increasingly challenging to manage and utilize the vast and dynamic pool of models effectively. We propose LO…
Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning
Yuchen Jiao, Jiin Woo, Gen Li +2
Average-reward reinforcement learning offers a principled framework for long-term decision-making by maximizing the mean reward per time step. Although Q-learning is a widely used…