Several tweets discuss the development of a self-improvement method for a Long-form Language Model (LLM) agent called ReAct. The method involves multi-step reasoning and integration of external information, aiming to enhance the agent's performance on complex natural language questions. The approach demonstrates the potential for improved reasoning and fine-tuning of LLM models without additional training costs.
[CL] ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent https://t.co/Q5serGteSZ This article describes a self-improvement method for a LLM agent called ReAct. The agent combines the ability for multi-step reasoning and integration of external information.… https://t.co/Wt3JoNMV6I
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent #AI #LLM https://t.co/KuKGKWdVSI https://t.co/OF536Wa54s
Self-Improvement for Multi-Step Reasoning LLM Agent Proposes a ReAct-style agent with self-critique for improving on the task of long-form question answering. It shows that the agent can be improved through ReST-style (reinforced self-training) iterative fine-tuning on its… https://t.co/vhXpJhwuwF
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent abs: https://t.co/J08nSobZcM Google DeepMind paper that demonstrates ReST-like AI feedback for reasoning agents, enabling a fine-tuned model to perform well on challenging compositional question-answering… https://t.co/loAfZmmrtg
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent paper page: https://t.co/6nb1q96q5u Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval… https://t.co/EcaCJCOPPF
Discover 'Think-on-Graph', a leap forward in LLM reasoning with knowledge graphs. Enhanced reasoning, updated knowledge, all without extra training costs - a breakthrough towards responsible AI: https://t.co/mkch5aJ1xa