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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…
Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning
Minwu Kim, Safal Shrestha, Anubhav Shrestha +1
As Reinforcement Learning with Verifiable Rewards (RLVR) substantially improves the reasoning abilities of large language models (LLMs), a new bottleneck emerges: more training pro…