Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Reinforcement01:23

Reinforcement

841
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
841
Observational Learning01:12

Observational Learning

841
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
841
Reinforcement Schedules01:24

Reinforcement Schedules

460
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
460
Reasoning01:30

Reasoning

400
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
400
Purposive Learning01:22

Purposive Learning

447
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
447
Inductive Reasoning00:59

Inductive Reasoning

65.2K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
65.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Effect of intermittent pneumatic compression on intracranial pressure in postoperative patients with severe traumatic brain injury.

The Journal of international medical research·2026
Same author

Gradient Mo Engineering in [100]-Oriented CuWO<sub>4</sub> Films for Boosted Photoelectrochemical Water Splitting.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Joule-heating synthesis of high-entropy oxides as efficient catalysts for electrochemical methanol oxidation.

Chemical communications (Cambridge, England)·2026
Same author

Mesonephric-like adenocarcinoma of the uterine corpus: a case report.

Frontiers in medicine·2026
Same author

WNT4 reprograms dental pulp stem cells to resist PANoptosis and rebuild neurogenic potential for facial nerve injury repair.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026
Same author

Monte Carlo investigation of spatiotemporal distortions in attosecond soft X-ray pulse focusing using a two-stage toroidal mirror system.

Optics express·2026

相关实验视频

Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

通过强化学习激励LLM中的推理

Daya Guo1, Dejian Yang1, Haowei Zhang1

  • 1DeepSeek-AI Team, Hangzhou, China.

Nature
|September 17, 2025
PubMed
概括

强化学习 (RL) 增强了大型语言模型 (LLM) 在没有人类数据的情况下的推理. 这种方法促进了先进的AI推理模式,以提高复杂任务的性能.

更多相关视频

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.4K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

相关实验视频

Last Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K
Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.4K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

科学领域:

  • 人工智能
  • 机器学习

背景情况:

  • 一般推理是人工智能的核心挑战.
  • 大型语言模型 (LLM) 和思维链 (CoT) 提示是有希望的,但需要大量的人类数据.
  • 目前的LLM能力不足以完成复杂的推理任务.

研究的目的:

  • 证明纯粹的强化学习 (RL) 可以增强LLM的推理能力.
  • 避免需要人为注释的推理轨迹.
  • 促进LLM中先进的推理模式的新兴发展.

主要方法:

  • 实施纯强化学习 (RL) 框架.
  • 培训使用RL的LLM来激励新出现的推理模式.
  • 评估经过RL培训的LLM在可验证任务中的表现.

主要成果:

  • 理性学习框架促进了自我反思和验证等新出现的推理模式.
  • 在数学,编码和STEM任务上超越了受监督的学习对手.
  • 来自大型模型的新兴推理模式可以增强小型模型的功能.

结论:

  • 纯粹的强化学习有效地增强了LLM的推理,而不需要人类演示.
  • 经过RL培训的LLM在复杂,可验证的任务中表现出卓越的表现.
  • 开发的RL框架为推进人工智能推理提供了可扩展的方法,并可以指导较小的模型.