DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Flashcards covering vocabulary and key concepts from the lecture on DeepSeek-R1 and its reasoning capabilities.

DeepSeek-R1-Zero

A first-generation reasoning model trained via large-scale reinforcement learning without supervised fine-tuning, exhibiting powerful reasoning capabilities.

Reinforcement Learning (RL)

A training paradigm where models learn to make decisions by receiving rewards or penalties for their actions.

Multi-stage Training

A training approach that involves multiple phases, each focusing on different aspects of model improvement.

Cold-start Data

Initial data used to fine-tune a model before reinforcement learning, aimed at providing a stable starting point.

Chain-of-Thought (CoT)

A reasoning technique where a model breaks down its thinking process step by step to arrive at a conclusion.

Self-evolution Process

The mechanism through which a model autonomously improves its reasoning abilities through reinforcement learning.

Self-verification

A capability where a model assesses its own outputs to ensure accuracy and correctness during reasoning tasks.

Distillation

The process of transferring the knowledge from a large, complex model to a smaller, more efficient model.

AI-powered Reasoning

The ability of artificial intelligence systems to solve complex problems using advanced reasoning techniques and algorithms.

Open-Sourcing Models

The practice of making machine learning models available to the public for use, modification, and distribution.

Performance Benchmarking

The evaluation process of comparing a model's performance against established datasets and metrics to measure effectiveness.

Consensus Voting

A technique used to improve model output reliability by selecting the most common answer from multiple generated responses.

Language Mixing

The phenomenon where a model randomly incorporates multiple languages into its responses, potentially affecting readability.

Majority Voting

An aggregation method where the result most frequently produced by a model across several outputs is selected as the final answer.

Reward Modeling

The process of defining and implementing reward signals that guide the training and behavior of reinforcement learning models.

Aha Moment

A pivotal point during learning when the model develops a significant insight or understanding regarding its performance or reasoning.