Categories: FAANG

Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo

Augmenting the multi-step reasoning abilities of Large Language Models (LLMs) has been a persistent challenge. Recently, verification has shown promise in improving solution consistency by evaluating generated outputs. However, current verification approaches suffer from sampling inefficiencies, requiring a large number of samples to achieve satisfactory performance. Additionally, training an effective verifier often depends on extensive process supervision, which is costly to acquire. In this paper, we address these limitations by introducing a novel verification method based on Twisted…
AI Generated Robotic Content

Recent Posts

Best guess as to which tools were used for this? VACE v2v?

credit to @ unreelinc submitted by /u/Leading_Primary_8447 [link] [comments]

4 hours ago

Calculating What Your Bank Spends on Marketing Compliance Reviews

By Taylor Mahoney, VP of Solutions ConsultingPicture this. The Federal Reserve has just dropped interest…

4 hours ago

AlphaGenome: AI for better understanding the genome

Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to…

4 hours ago

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining

This paper was accepted to the ACL 2025 main conference as an oral presentation. This…

4 hours ago

Build an intelligent multi-agent business expert using Amazon Bedrock

In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in…

4 hours ago

How Schroders built its multi-agent financial analysis research assistant

Financial analysts spend hours grappling with ever-increasing volumes of market and company data to extract…

4 hours ago