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cs.CL2025
DySK-Attn: A Framework for Efficient, Real-Time Knowledge Updating in Large Language Models via Dynamic Sparse Knowledge Attention
Kabir Khan, Priya Sharma, Arjun Mehta +2
Large Language Models (LLMs) suffer from a critical limitation: their knowledge is static and quickly becomes outdated. Retraining these massive models is computationally prohibiti…
cs.CL2025
When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents
Mrinal Rawat, Arkajyoti Chakraborty, Neha Gupta +1
Supervised fine-tuning (SFT) has emerged as one of the most effective ways to improve the performance of large language models (LLMs) in downstream tasks. However, SFT can have dif…