5 papers
Mixture-of-Experts Language Models Can Be Strong and Efficient Retrievers
Anubhav Shrestha, Safal Shrestha, Minwu Kim +2
Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectiveness improving as backbones grow…
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
Safal Shrestha, Anubhav Shrestha, Aadim Nepal +2
Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or…
Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training
Aadim Nepal, Safal Shrestha, Anubhav Shrestha +4
Large language models improve at math after instruction tuning, reinforcement learning, or knowledge distillation. We ask whether these gains come from major changes in the transfo…
Reinforcement Learning vs. Distillation: Understanding Accuracy and Capability in LLM Reasoning
Minwu Kim, Anubhav Shrestha, Safal Shrestha +2
Recent studies have shown that reinforcement learning with verifiable rewards (RLVR) enhances overall accuracy (pass@1) but often fails to improve capability (pass@k) of LLMs in re…
Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings
Safal Shrestha, Minwu Kim, Aadim Nepal +2
Designing effective reasoning-capable LLMs typically requires training using Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with carefully curated Long Chain…