New ReLIT framework blends explicit and implicit reasoning for LLMs

Researchers have introduced ReLIT, a novel framework that combines explicit Chain-of-Thought prompting with implicit latent reasoning for large language models. This hybrid approach uses a lightweight recursive block to refine internal latent states before generating an output, aiming to reduce the computational overhead of traditional CoT methods. ReLIT, when augmenting a frozen TinyLlama-1.1B backbone, demonstrates parameter efficiency and strong performance on logical reasoning benchmarks like GLoRE, ProofWriter, and RuleTaker, suggesting that reasoning can be scaled through depth rather than width. AI

IMPACT This research offers a more efficient approach to LLM reasoning, potentially reducing computational costs and improving performance on complex tasks.

RANK_REASON The cluster contains a research paper detailing a new framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Chain-of-Thought
• GLoRE
• Hugging Face
• Large Language Models
• ProofWriter
• Recursive Latent Implicit Transformer
• RuleTaker
• TinyLlama-1.1B
• Tiny Recursive Models

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →