Reinforcement Learning does NOT make the base model more intelligent and limits the world of the base model in exchange for early pass performances. Graphs show that after pass 1000 the reasoning ...

Imagine trying to teach a child how to solve a tricky math problem. You might start by showing them examples, guiding them step by step, and encouraging them to think critically about their approach.

A peer-reviewed paper about Chinese startup DeepSeek's models explains their training approach but not how they work through ...

The architecture of FOCUS. Given offline data, FOCUS learns a $p$ value matrix by KCI test and then gets the causal structure by choosing a $p$ threshold. After ...

“We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT ...

Researchers from Fudan University and Shanghai AI Laboratory have conducted an in-depth analysis of OpenAI’s o1 and o3 models, shedding light on their advanced reasoning capabilities. These models, ...

DeepSeek-R1's release last Monday has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. Matching OpenAI’s o1 at just 3%-5% ...

An analysis by Epoch AI, a nonprofit AI research institute, suggests the AI industry may not be able to eke massive performance gains out of reasoning AI models for much longer. As soon as within a ...

The new framework from Tongyi Lab enables agents to create their own training data by exploring and interacting with new software environments.

Using several recent innovations, the company Databricks will let customers boost the IQ of their AI models even if they don’t have squeaky clean data. Databricks, a company that helps big businesses ...