OGGlossary OSVocabulario de Solana reconstruido 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
Lectura rápida

Lectura rápida

Empieza por la explicación más corta y útil antes de profundizar.

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

Usa primero la analogía corta para razonar mejor sobre el término cuando aparezca en código, docs o prompts.

Piensa en esto como una pieza de la pila de contexto o inferencia usada en productos con agentes o LLMs.

Contexto técnico

Contexto técnico

Ubica el término dentro de la capa de Solana en la que vive para razonar mejor sobre él.

LLMs, RAG, embeddings, inferencia y primitivas orientadas a agentes.

Por qué le importa a un builder

Por qué le importa a un builder

Convierte el término de vocabulario en algo operacional para producto e ingeniería.

Este término desbloquea conceptos adyacentes rápido, así que funciona mejor cuando lo tratas como un punto de conexión y no como una definición aislada.

Handoff para IA

Handoff para IA

Usa este bloque compacto cuando quieras dar contexto sólido a un agente o asistente sin volcar toda la página.

Reasoning Model (reasoning-model)
Categoría: IA / ML
Definición: 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 Lenguaje Grande), Inference
Glossary Copilot

Haz preguntas de Solana con contexto aterrizado sin salir del glosario.

Usa contexto del glosario, relaciones entre términos, modelos mentales y builder paths para recibir respuestas estructuradas en vez de output genérico.

Abrir workspace completa del Copilot
Explicar este código

Opcional: pega código Anchor, Solana o Rust para que el Copilot mapee primitivas de vuelta al glosario.

Haz una pregunta aterrizada en el glosario

Haz una pregunta aterrizada en el glosario

El Copilot responderá usando el término actual, conceptos relacionados, modelos mentales y el grafo alrededor del glosario.

Grafo conceptual

Ve el término como parte de una red, no como una definición aislada.

Estas ramas muestran qué conceptos toca este término directamente y qué existe una capa más allá de ellos.

Rama

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.

Ingeniería de PromptStructured Output
Abrir término
Rama

LLM (Modelo de Lenguaje 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
Abrir término
Rama

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)
Abrir término
Siguientes conceptos para explorar

Mantén la cadena de aprendizaje en movimiento en lugar de parar en una sola definición.

Estos son los siguientes conceptos que vale la pena abrir si quieres que este término tenga más sentido dentro de un 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 término
IA / ML

LLM (Modelo de Lenguaje 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 término
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 término
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.

Abrir término
Comúnmente confundido con

Términos cercanos en vocabulario, acrónimo o vecindad conceptual.

Estas entradas son fáciles de mezclar cuando lees rápido, haces prompting a un LLM o estás entrando en una nueva capa 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
Abrir término
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 término
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
Abrir término
Términos relacionados

Sigue los conceptos que realmente le dan contexto a este término.

Las entradas del glosario se vuelven útiles cuando están conectadas. Estos enlaces son el camino más corto hacia ideas adyacentes.

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 término
IA / MLllm

LLM (Modelo de Lenguaje 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 término
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 término
Más en la categoría

Quédate en la misma capa y sigue construyendo contexto.

Estas entradas viven junto al término actual y ayudan a que la página se sienta parte de un grafo de conocimiento más amplio en lugar de un callejón sin salida.

IA / ML

LLM (Modelo de Lenguaje 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 término
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 término
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 término
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 término