1 citations · 1 across the 2 of their papers we have counts for
2 papers
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…
cs.AI2025★ 1 cited
Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
Mrinal Rawat, Ambuje Gupta, Rushil Goomer +3
The ReAct (Reasoning + Action) capability in large language models (LLMs) has become the foundation of modern agentic systems. Recent LLMs, such as DeepSeek-R1 and OpenAI o1/o3, ex…