2nd/10_Wiki/Topics/AI_Sampling_Strategies.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-ai-sampling-strategies	AI Sampling Strategies	10_Wiki/Topics	verified	self	LLM Sampling Decoding Strategies	none	A	0.9	applied	llm sampling decoding inference		2026-05-10	pending	language framework python vllm/transformers

AI Sampling Strategies

매 한 줄

"매 logits → token 의 conversion 의 art — 매 quality vs diversity trade-off." 매 greedy 의 deterministic — 매 temperature/top-k/top-p 의 stochastic — 매 2026 에 min-p, mirostat, speculative decoding 의 mainstream.

매 핵심

매 deterministic

• Greedy: argmax token. 매 repetitive 의 risk.
• Beam search: 매 top-B sequences 의 maintain. 매 translation 의 useful, 매 open-ended 의 bland.

매 stochastic

• Temperature: logits / T. T<1 sharpen, T>1 flatten.
• Top-k: 매 top-k tokens 의 sample.
• Top-p (nucleus): 매 cumulative prob ≥ p 의 smallest set.
• Min-p: 매 P(top) * min_p 의 threshold — 매 top-p 의 better.
• Typical-p: 매 entropy-based — 매 typical tokens.
• Mirostat: 매 perplexity targeting feedback control.

매 응용

1. Creative writing — 매 high temp + top-p.
2. Code generation — 매 low temp + greedy fallback.
3. Reasoning — 매 self-consistency (sample N, majority vote).

💻 패턴

Temperature + top-p

import torch
import torch.nn.functional as F

def sample(logits, temperature=0.7, top_p=0.9):
    logits = logits / temperature
    probs = F.softmax(logits, dim=-1)
    sorted_probs, sorted_idx = probs.sort(descending=True)
    cumsum = sorted_probs.cumsum(-1)
    mask = cumsum - sorted_probs > top_p
    sorted_probs[mask] = 0
    sorted_probs /= sorted_probs.sum()
    pick = torch.multinomial(sorted_probs, 1)
    return sorted_idx.gather(-1, pick)

Min-p

def min_p_sample(logits, min_p=0.05, temperature=1.0):
    logits = logits / temperature
    probs = F.softmax(logits, dim=-1)
    threshold = probs.max(-1, keepdim=True).values * min_p
    probs = torch.where(probs >= threshold, probs, torch.zeros_like(probs))
    probs /= probs.sum(-1, keepdim=True)
    return torch.multinomial(probs, 1)

Self-consistency (CoT majority vote)

def self_consistency(prompt, llm, n=20):
    answers = []
    for _ in range(n):
        cot = llm.generate(prompt, temperature=0.7)
        answers.append(extract_final_answer(cot))
    from collections import Counter
    return Counter(answers).most_common(1)[0][0]

Speculative decoding

def speculative(target, draft, prompt, k=4):
    # Draft k tokens cheaply, target verifies in parallel
    ctx = prompt
    while not done(ctx):
        draft_tokens, draft_probs = draft.generate(ctx, k)
        target_probs = target.score(ctx, draft_tokens)
        accepted = []
        for i, (dp, tp) in enumerate(zip(draft_probs, target_probs)):
            r = torch.rand(1)
            if r < min(1, tp / dp):
                accepted.append(draft_tokens[i])
            else:
                # Reject, sample from (target - draft)+ then break
                resample = sample_diff(target_probs[i], draft_probs[i])
                accepted.append(resample)
                break
        else:
            accepted.append(target.sample(ctx + accepted))
        ctx += accepted
    return ctx

Mirostat (perplexity control)

def mirostat(logits, mu, tau=5.0, eta=0.1):
    # Adaptively adjusts top-k to target surprise tau
    sorted_probs, idx = F.softmax(logits, -1).sort(descending=True)
    s = -torch.log(sorted_probs)
    k = (s < mu).sum().item()
    k = max(k, 1)
    pick = torch.multinomial(sorted_probs[:k] / sorted_probs[:k].sum(), 1)
    surprise = -torch.log(sorted_probs[pick])
    mu = mu - eta * (surprise - tau)
    return idx[pick], mu

Repetition penalty

def apply_repetition_penalty(logits, generated_ids, penalty=1.1):
    for tok in set(generated_ids):
        if logits[tok] < 0:
            logits[tok] *= penalty
        else:
            logits[tok] /= penalty
    return logits

매 결정 기준

상황	Sampler
Code, math, structured	T=0 greedy or T=0.2
Chat / general	T=0.7, top-p=0.9 or min-p=0.05
Creative / fiction	T=1.0+, min-p=0.02
Reasoning ensemble	T=0.7, n=20, majority vote
Translation	Beam search (B=4-8)
Latency-critical	Speculative decoding (target + small draft)

기본값: 매 T=0.7 + min-p=0.05.

🔗 Graph

• 부모: LLM Inference · Decoding
• 변형: Beam Search · Nucleus Sampling · Mirostat
• 응용: Self-Consistency · Speculative Decoding · CoT
• Adjacent: vLLM · Temperature · Repetition Penalty

🤖 LLM 활용

언제: 매 inference pipeline 의 every call — 매 task 의 sampler 의 match. 언제 X: 매 logprob analysis (no sampling needed).

❌ 안티패턴

• High temp + greedy fallback: 매 inconsistent — 매 single sampler.
• Top-k=1 with high temp: 매 contradictory.
• No repetition penalty on long outputs: 매 loops.
• Speculative without acceptance check: 매 distribution shift.

🧪 검증 / 중복

• Verified (Holtzman et al. nucleus sampling 2020, Leviathan et al. speculative 2023, min-p paper 2024).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — sampler taxonomy + working code