4 citations · 5 across the 12 of their papers we have counts for
16 papers
Reasoning-Preserving Fine-Tuning of Post-RL LLMs with Null-Basis LoRA
Wenzhi Fang, Nicholas Tzou, Lazar Valkov +1
Reinforcement learning (RL)-based post-training has become an effective approach for eliciting reasoning capabilities in large language models (LLMs). However, adapting post-RL mod…
Not All Ranks Are Equal: Budget-Aware LoRA Merging Across Tasks
Avinash Amballa, Yashas Malur Saidutta, Wenbo Li +2
Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging methods assume every layer needs…
Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering
Aounon Kumar, Sudipta Paul, Vivek Kulkarni +2
The effective use of search engines by large language models (LLMs) remains a significant challenge, particularly in complex, multi-hop question-answering (MHQA) tasks. These tasks…
PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni +4
Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards…
TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory
Tianyu Yang, Sudipta Paul, Vijay Srinivasan +2
Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. Existing memory agents active…
Latent Personal Memory: Represent personal memory as dynamic soft prompts
Debrup Das, Avinash Amballa, Yashas Malur Saidutta +3
Personalizing large language models (LLMs) requires encoding long-term, user-specific behavioral patterns in a way that is computationally efficient, scalable, and compatible with…