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Tree-of-Thought (ToT)
Tree-of-Thought structures reasoning as a search over branching steps. An LLM expands candidate thoughts, a controller scores and…
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Mixture of Experts (MoE)
Mixture of Experts (MoE) scales model capacity by routing each token to a small subset of expert networks,…
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Chain-of-Thought (CoT)
Chain-of-thought (CoT) prompts models to show intermediate reasoning steps, improving multi-step problem solving and interpretability for math, logic,…
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Direct Preference Optimization (DPO)
DPO aligns LLMs using human preference pairs—no reward model or RL required—by training the policy to prefer chosen…
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Small Language Model (SLM)
A Small Language Model (SLM) is a compact LLM optimized for low latency and memory via distillation, pruning,…
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In-Context Learning (ICL)
ICL lets LLMs infer tasks from prompt-only examples—no weight updates—enabling zero/few-shot classification, extraction, and reasoning with schema-following in…
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Reinforcement Learning from Human Feedback (RLHF)
RLHF aligns language models by training a reward model on human preferences and optimizing the policy with RL…
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Instruction Tuning
Instruction tuning fine-tunes LMs on instruction–response pairs to improve adherence, helpfulness, and controllability, and often precedes preference tuning…
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Text Embedding
A text embedding is a dense vector that encodes the meaning of text for similarity search, clustering, and…
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Tree-of-Thought (ToT)
Tree-of-Thought structures reasoning as a search over branching steps. An LLM expands candidate thoughts, a controller scores and…
-
Mixture of Experts (MoE)
Mixture of Experts (MoE) scales model capacity by routing each token to a small subset of expert networks,…
-
Chain-of-Thought (CoT)
Chain-of-thought (CoT) prompts models to show intermediate reasoning steps, improving multi-step problem solving and interpretability for math, logic,…
-
Direct Preference Optimization (DPO)
DPO aligns LLMs using human preference pairs—no reward model or RL required—by training the policy to prefer chosen…
-
Small Language Model (SLM)
A Small Language Model (SLM) is a compact LLM optimized for low latency and memory via distillation, pruning,…
-
In-Context Learning (ICL)
ICL lets LLMs infer tasks from prompt-only examples—no weight updates—enabling zero/few-shot classification, extraction, and reasoning with schema-following in…
-
Reinforcement Learning from Human Feedback (RLHF)
RLHF aligns language models by training a reward model on human preferences and optimizing the policy with RL…
-
Instruction Tuning
Instruction tuning fine-tunes LMs on instruction–response pairs to improve adherence, helpfulness, and controllability, and often precedes preference tuning…
-
Text Embedding
A text embedding is a dense vector that encodes the meaning of text for similarity search, clustering, and…