OGGlossary OSVocabulário de Solana reconstruído para builders.
ENPT-BRES
IA / ML

Reasoning Model

A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.

IDreasoning-modelAliasThinking ModelAliaso1Aliaso3
Leitura rápida

Leitura rápida

Comece pela explicação mais curta e útil antes de aprofundar.

A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.

Modelo mental

Modelo mental

Use primeiro a analogia curta para raciocinar melhor sobre o termo quando ele aparecer em código, docs ou prompts.

Pense nisso como uma peça da pilha de contexto ou inferência usada em produtos com agentes ou LLMs.

Contexto técnico

Contexto técnico

Coloque o termo dentro da camada de Solana em que ele vive para raciocinar melhor sobre ele.

LLMs, RAG, embeddings, inferência e primitivas voltadas a agentes.

Por que builders ligam para isso

Por que builders ligam para isso

Transforme o termo de vocabulário em algo operacional para produto e engenharia.

Este termo destrava conceitos adjacentes rapidamente, então funciona melhor quando você o trata como um ponto de conexão, não como definição isolada.

Handoff para IA

Handoff para IA

Use este bloco compacto quando quiser dar contexto aterrado para um agente ou assistente sem despejar a página inteira.

Reasoning Model (reasoning-model)
Categoria: IA / ML
Definição: A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.
Aliases: Thinking Model, o1, o3
Relacionados: Chain-of-Thought (CoT), LLM (Modelo de Linguagem Grande), Inference
Glossary Copilot

Faça perguntas de Solana com contexto aterrado sem sair do glossário.

Use contexto do glossário, relações entre termos, modelos mentais e builder paths para receber respostas estruturadas em vez de output genérico.

Abrir workspace completa do Copilot
Explicar este código

Opcional: cole código Anchor, Solana ou Rust para o Copilot mapear primitivas de volta para termos do glossário.

Faça uma pergunta aterrada no glossário

Faça uma pergunta aterrada no glossário

O Copilot vai responder usando o termo atual, conceitos relacionados, modelos mentais e o grafo ao redor do glossário.

Grafo conceitual

Veja o termo como parte de uma rede, não como uma definição sem saída.

Esses ramos mostram quais conceitos esse termo toca diretamente e o que existe uma camada além deles.

Ramo

Chain-of-Thought (CoT)

A prompting technique or model-native capability where the LLM produces intermediate reasoning steps before arriving at a final answer, improving accuracy on multi-step problems. Originally a prompting strategy ('think step by step'), CoT is now built directly into reasoning models like o1 and DeepSeek-R1 as an internal process. When combining CoT with structured output, developers should place reasoning fields before answer fields to avoid bypassing the reasoning process.

Engenharia de PromptStructured Output
Abrir termo
Ramo

LLM (Modelo de Linguagem Grande)

A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.

TransformerFoundation Model
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Ramo

Inference

The process of running a trained model on new inputs to generate predictions or outputs. Inference is the 'using' phase (vs. training). Inference cost depends on model size, input/output token count, and hardware (GPUs/TPUs). API providers (Anthropic, OpenAI) charge per token for inference. On-device inference (llama.cpp, GGUF) runs locally without API calls.

Token (AI/NLP)
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Próximos conceitos para explorar

Continue a cadeia de aprendizado em vez de parar em uma única definição.

Estes são os próximos conceitos que valem abrir se você quiser que este termo faça mais sentido dentro de um workflow real de Solana.

IA / ML

Chain-of-Thought (CoT)

A prompting technique or model-native capability where the LLM produces intermediate reasoning steps before arriving at a final answer, improving accuracy on multi-step problems. Originally a prompting strategy ('think step by step'), CoT is now built directly into reasoning models like o1 and DeepSeek-R1 as an internal process. When combining CoT with structured output, developers should place reasoning fields before answer fields to avoid bypassing the reasoning process.

Abrir termo
IA / ML

LLM (Modelo de Linguagem Grande)

A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.

Abrir termo
IA / ML

Inference

The process of running a trained model on new inputs to generate predictions or outputs. Inference is the 'using' phase (vs. training). Inference cost depends on model size, input/output token count, and hardware (GPUs/TPUs). API providers (Anthropic, OpenAI) charge per token for inference. On-device inference (llama.cpp, GGUF) runs locally without API calls.

Abrir termo
IA / ML

RLHF (Reinforcement Learning from Human Feedback)

A training technique that aligns LLM outputs with human preferences. Process: (1) train a reward model from human comparisons of outputs, (2) use reinforcement learning (PPO) to optimize the LLM against the reward model. RLHF makes models more helpful, harmless, and honest. Used by Claude, ChatGPT, and other assistants. Alternatives include DPO (Direct Preference Optimization) and Constitutional AI.

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Comumente confundido com

Termos próximos em vocabulário, sigla ou vizinhança conceitual.

Essas entradas são fáceis de misturar quando você lê rápido, faz prompting em um LLM ou está entrando em uma nova camada de Solana.

IA / MLdiffusion-model

Diffusion Model

A generative AI architecture that creates images, video, or audio by learning to reverse a noise-adding process—starting from pure noise and iteratively denoising to produce coherent output. Diffusion models power leading image generators (Stable Diffusion, DALL-E 3, Midjourney) and video generators (Sora). Key variants include latent diffusion (operating in compressed space) and diffusion transformers (DiT).

AliasLatent DiffusionAliasDiT
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IA / MLfoundation-model

Foundation Model

A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.

Abrir termo
IA / MLstate-space-model

State Space Model (Mamba)

An alternative to the Transformer architecture that processes sequences with linear O(n) complexity instead of quadratic O(n^2) attention, enabling efficient handling of very long sequences. Mamba introduced selective state spaces where the model dynamically filters information based on content. Hybrid architectures like Jamba combine SSM efficiency with attention's retrieval strength.

AliasSSMAliasMamba
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Termos relacionados

Siga os conceitos que realmente dão contexto a este termo.

Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.

IA / MLchain-of-thought

Chain-of-Thought (CoT)

A prompting technique or model-native capability where the LLM produces intermediate reasoning steps before arriving at a final answer, improving accuracy on multi-step problems. Originally a prompting strategy ('think step by step'), CoT is now built directly into reasoning models like o1 and DeepSeek-R1 as an internal process. When combining CoT with structured output, developers should place reasoning fields before answer fields to avoid bypassing the reasoning process.

Abrir termo
IA / MLllm

LLM (Modelo de Linguagem Grande)

A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.

Abrir termo
IA / MLinference

Inference

The process of running a trained model on new inputs to generate predictions or outputs. Inference is the 'using' phase (vs. training). Inference cost depends on model size, input/output token count, and hardware (GPUs/TPUs). API providers (Anthropic, OpenAI) charge per token for inference. On-device inference (llama.cpp, GGUF) runs locally without API calls.

Abrir termo
Mais na categoria

Permaneça na mesma camada e continue construindo contexto.

Essas entradas vivem ao lado do termo atual e ajudam a página a parecer parte de um grafo maior, não um beco sem saída.

IA / ML

LLM (Modelo de Linguagem Grande)

A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.

Abrir termo
IA / ML

Transformer

The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).

Abrir termo
IA / ML

Attention Mechanism

A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.

Abrir termo
IA / ML

Foundation Model

A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.

Abrir termo