\n| \u590d\u6742\u63a8\u7406\u8868\u73b0 (AIME\/Codeforces)<\/td>\n | \u51c6\u786e\u7387\u5728 20% ~ 40% \u5f98\u5f8a\u505c\u6ede<\/td>\n | 🏆 \u7a81\u7834 85% ~ 95%（\u903c\u8fd1\u4eba\u7c7b\u9876\u5c16\u7ade\u8d5b\u9009\u624b\u6c34\u51c6）<\/td>\n | \u5728\u975e\u5e73\u51e1\u903b\u8f91\u95ee\u9898\u4e0a\u5b9e\u73b0\u8d28\u7684\u4ee3\u5dee\u8de8\u8d8a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n\u4e8c\u3001\u6d4b\u8bd5\u671f\u8ba1\u7b97（Test-Time Compute）\u4e24\u5927\u6838\u5fc3\u6269\u5c55\u901a\u8def\u65f6\u5e8f\u67b6\u6784<\/h3>\n\u5728\u5b66\u672f\u754c\u4e0e\u5de5\u4e1a\u754c\u7684\u524d\u6cbf\u63a2\u7d22\u4e2d，\u6269\u5c55\u6d4b\u8bd5\u671f\u7b97\u529b\u4e3b\u8981\u6cbf\u7740\u4e24\u6761\u622a\u7136\u4e0d\u540c\u4f46\u53c8\u76f8\u4e92\u878d\u5408\u7684\u6280\u672f\u8def\u5f84\u5c55\u5f00：<\/p>\n [🚀 \u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55\u4e24\u5927\u6d41\u6d3e]
\n |
\n +——————————–+——————————–+
\n | |
\n v (\u8def\u5f84 A: \u641c\u7d22\u7b97\u6cd5\u9a71\u52a8) v (\u8def\u5f84 B: \u81ea\u56de\u5f52\u539f\u751f\u601d\u8003 Token)
\n+————————————————–+ +————————————————–+
\n| 🌟 1. \u5916\u90e8\u663e\u5f0f\u6811\u641c\u7d22 (Explicit Tree Search) | | 🌟 2. \u5185\u751f\u9690\u5f0f\u601d\u7ef4\u94fe (Endogenous Thinking Tokens)|
\n| – \u7ed3\u5408 PRM \u8fc7\u7a0b\u5956\u52b1\u6a21\u578b\u4e0e MCTS \u8499\u7279\u5361\u6d1b\u6811\u641c\u7d22 | | – \u901a\u8fc7\u5f3a\u5316\u5b66\u4e60\u8bad\u7ec3\u6a21\u578b\u81ea\u4e3b\u751f\u6210 `<think>…<\/think>`|
\n| – \u5728\u89e3\u7a7a\u95f4\u663e\u5f0f\u5c55\u5f00\u591a\u4e2a\u63a8\u7406\u5206\u652f\u5e76\u6253\u5206\u8bc4\u4f30\u526a\u679d | | – \u6a21\u578b\u81ea\u4e3b\u5728\u8349\u7a3f\u7eb8\u4e0a\u8fdb\u884c\u9a8c\u7b97\u3001\u63a8\u7ffb\u5047\u8bbe\u4e0e\u91cd\u65b0\u89c4\u5212 |
\n+————————————————–+ +————————————————–+
\n | |
\n \\\\ \/
\n \\\\ \/
\n v v
\n+—————————————————————————————————+
\n| 🏆 \u7ec8\u6781\u6269\u5c55\u5b9a\u5f8b (Test-Time Compute Scaling Law): |
\n| \u5728\u590d\u6742\u63a8\u7406\u4efb\u52a1\u4e2d，\u5c06\u6d4b\u8bd5\u671f\u7b97\u529b（Thinking Tokens \u6570\u91cf\u6216\u91c7\u6837\u5019\u9009\u6570）\u63d0\u5347 100 \u500d， |
\n| \u5176\u5e26\u6765\u7684\u51c6\u786e\u7387\u8dc3\u8fc1\u6548\u679c，\u7b49\u4ef7\u4e8e\u5c06\u9884\u8bad\u7ec3\u6a21\u578b\u53c2\u6570\u91cf\u7269\u7406\u6269\u5927 10 \u500d\u4ee5\u4e0a！ |
\n+—————————————————————————————————+<\/p>\n
\n\u4e09\u3001\u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55\u7684\u6570\u5b66\u672c\u8d28\u4e0e\u635f\u5931\u8bbe\u8ba1<\/h3>\n1. \u4f20\u7edf\u81ea\u56de\u5f52\u751f\u6210\u7684\u6700\u5927\u4f3c\u7136\u6982\u7387<\/h4>\n\u4f20\u7edf\u81ea\u56de\u5f52\u6a21\u578b\u7684\u751f\u6210\u76ee\u6807\u662f\u76f4\u63a5\u6700\u5927\u5316\u6700\u7ec8\u7b54\u6848 $Y = (y_1, y_2, \\\\dots, y_M)$ \u5728\u7ed9\u5b9a\u8f93\u5165 $X$ \u4e0b\u7684\u6761\u4ef6\u8054\u5408\u6982\u7387：$$P(Y | X) = \\\\prod_{j=1}^{M} P(y_j | X, y_{<j})$$<\/p>\n \u5728\u6b64\u8bbe\u5b9a\u4e0b，\u6a21\u578b\u5fc5\u987b\u5728\u7b2c\u4e00\u4e2a Token $y_1$ \u5904\u5c31\u201c\u8d4c\u5bf9\u201d\u6700\u7ec8\u7684\u5168\u5c40\u89e3\u6cd5\u8def\u5f84，\u5bb9\u9519\u7387\u4e3a\u96f6\u3002<\/p>\n 2. \u5f15\u5165\u4e2d\u95f4\u601d\u8003\u53d8\u91cf（Thinking Latent Variable）\u7684\u8fb9\u9645\u79ef\u5206<\/h4>\n\u5728\u6df1\u5ea6\u601d\u8003\u6a21\u578b\u4e2d，\u5f15\u5165\u4e86\u4e2d\u95f4\u9690\u5f0f\u63a8\u7406\u94fe $Z = (z_1, z_2, \\\\dots, z_K)$（\u5176\u4e2d $z_k$ \u4ee3\u8868\u601d\u8003 Token）：$$P(Y | X) = \\\\sum_{Z \\\\in \\\\mathcal{Z}} P(Y, Z | X) = \\\\sum_{Z \\\\in \\\\mathcal{Z}} P(Z | X) \\\\cdot P(Y | X, Z)$$<\/p>\n \u6a21\u578b\u5728\u8f93\u51fa\u6700\u7ec8\u7b54\u6848 $Y$ \u4e4b\u524d，\u5148\u5728\u9690\u5f0f\u7a7a\u95f4 $\\\\mathcal{Z}$ \u4e2d\u751f\u6210\u957f\u8fbe\u6570\u5343\u6b65\u7684\u63a8\u5bfc\u8349\u7a3f $Z$\u3002\u901a\u8fc7\u5f3a\u5316\u5b66\u4e60（\u5982 GRPO \/ PPO）\u6700\u5927\u5316\u53ef\u9a8c\u8bc1\u4efb\u52a1（\u5982\u6570\u5b66\u7b54\u6848\u6b63\u786e\u6027\u3001\u5355\u5143\u6d4b\u8bd5\u901a\u8fc7\u7387）\u7684\u7ec8\u7aef\u5956\u52b1（Terminal Reward），\u6a21\u578b\u81ea\u4e3b\u5b66\u4f1a\u4e86：<\/p>\n \n- \u201c\u7b49\u7b49，\u6211\u521a\u624d\u7684\u5047\u8bbe\u662f\u9519\u7684，\u91cd\u65b0\u8ba1\u7b97\u4e00\u904d\u201d（\u63a2\u7d22\u4e0e\u56de\u6eaf）；<\/li>\n
- \u201c\u8ba9\u6211\u4eec\u7528\u4ee3\u5165\u6cd5\u68c0\u9a8c\u4e00\u4e0b\u521a\u624d\u7684\u6839\u201d（\u81ea\u7701\u4e0e\u9a8c\u8bc1）；<\/li>\n
- \u201c\u6362\u4e00\u79cd\u51e0\u4f55\u5750\u6807\u7cfb\u6c42\u89e3\u201d（\u7b56\u7565\u8dc3\u8fc1）\u3002<\/li>\n<\/ul>\n
\n\u56db\u3001\u751f\u4ea7\u7ea7 PyTorch \u6d4b\u8bd5\u671f\u7b97\u529b\u6269\u5c55（Best-of-N \u4e0e\u6e29\u5ea6\u7f29\u653e）\u5bf9\u6bd4\u5b9e\u9a8c<\/h3>\n\u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u9a8c\u73af\u5883\u4e2d\u6a21\u62df\u5e76\u5bf9\u6bd4：<\/p>\n - \u5355\u6b21\u81ea\u56de\u5f52\u76f4\u63a5\u8d2a\u5fc3\u751f\u6210（Baseline System 1）；<\/li>\n
- \u6d4b\u8bd5\u671f\u91c7\u6837\u8ba1\u7b97\u6269\u5c55（Test-Time Compute: Best-of-N with Verifier）\u3002<\/li>\n
"""
\ntest_time_compute_scaling_experiment.py
\n\u5927\u6a21\u578b\u6d4b\u8bd5\u671f\u8ba1\u7b97 (Test-Time Compute) \u7269\u7406\u6269\u5c55\u5bf9\u6bd4\u5b9e\u9a8c：
\n\u57fa\u4e8e\u5019\u9009\u751f\u6210\u3001\u8fc7\u7a0b\u9a8c\u8bc1\u5668\u6253\u5206\u4e0e\u89e3\u7a7a\u95f4\u91cd\u6392\u5e8f\u5b9e\u6218
\n"""<\/p>\n import math
\nimport torch
\nimport torch.nn as nn
\nimport torch.nn.functional as F
\nfrom typing import List, Dict, Tuple<\/p>\n class MockReasoningLLM(nn.Module):
\n """\u6a21\u62df\u5927\u6a21\u578b\u63a8\u7406\u524d\u5411\u7f51\u7edc：\u8f93\u51fa Logits \u4e0e\u9690\u5c42\u8868\u5f81"""<\/p>\n def __init__(self, vocab_size: int = 1000, hidden_dim: int = 256):
\n super().__init__()
\n self.embedding = nn.Embedding(vocab_size, hidden_dim)
\n self.transformer_layer = nn.TransformerEncoderLayer(
\n d_model=hidden_dim, nhead=8, dim_feedforward=512, batch_first=True
\n )
\n self.lm_head = nn.Linear(hidden_dim, vocab_size)<\/p>\n def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
\n # [Batch, SeqLen, Hidden]
\n x = self.embedding(input_ids)
\n h = self.transformer_layer(x)
\n logits = self.lm_head(h)
\n return logits<\/p>\n class TestTimeComputeEvaluator:
\n """\u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55\u5f15\u64ce：\u652f\u6301\u5355\u6b21\u8d2a\u5fc3\u4e0e Best-of-N \u7b97\u529b\u6269\u5c55"""<\/p>\n def __init__(self, model: MockReasoningLLM):
\n self.model = model
\n self.model.eval()<\/p>\n def simulate_verify_solution(self, solution_tokens: List[int], ground_truth: int = 42) -> float:
\n """
\n \u6a21\u62df\u5916\u90e8\u8f7b\u91cf\u9a8c\u8bc1\u5668（Verifier \/ PRM）：
\n \u5bf9\u63a8\u5bfc\u51fa\u7684\u6700\u7ec8\u7b54\u6848\u8fdb\u884c\u6570\u5b66\u65ad\u8a00\u6216\u89c4\u5219\u6253\u5206，\u8fd4\u56de [0.0 ~ 1.0] \u7684\u7f6e\u4fe1\u5ea6
\n """
\n # \u7b80\u5316\u6a21\u62df：\u5047\u8bbe token \u548c\u4e3a\u7279\u5b9a\u503c\u65f6\u4e3a\u6b63\u786e\u7b54\u6848
\n predicted_val = sum(solution_tokens) % 100
\n if predicted_val == ground_truth:
\n return 1.0 # 100% \u547d\u4e2d\u771f\u503c
\n # \u5426\u5219\u7ed9\u51fa\u57fa\u4e8e\u8ddd\u79bb\u7684\u8f6f\u542f\u53d1\u5f0f\u5206\u6570
\n diff = abs(predicted_val – ground_truth)
\n return math.exp(-0.1 * diff)<\/p>\n @torch.no_grad()
\n def generate_single_greedy(self, prompt_tokens: torch.Tensor, max_new_tokens: int = 16) -> List[int]:
\n """\u57fa\u7ebf\u65b9\u6cd5：\u4f20\u7edf\u5355\u5411\u81ea\u56de\u5f52\u8d2a\u5fc3\u751f\u6210 (Zero Test-Time Compute Extension)"""
\n curr = prompt_tokens.clone()
\n generated = []
\n for _ in range(max_new_tokens):
\n logits = self.model(curr)
\n next_token = torch.argmax(logits[:, -1, :], dim=-1, keepdim=True)
\n curr = torch.cat([curr, next_token], dim=1)
\n generated.append(next_token.item())
\n return generated<\/p>\n @torch.no_grad()
\n def generate_best_of_n(
\n self, prompt_tokens: torch.Tensor, n_candidates: int = 16, temperature: float = 0.8, max_new_tokens: int = 16
\n ) -> Tuple[List[int], float, int]:
\n """
\n \u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55：\u751f\u6210 N \u6761\u72ec\u7acb\u601d\u8003\u8def\u5f84，\u5e76\u7531\u9a8c\u8bc1\u5668\u9009\u51fa\u6700\u4f18\u89e3
\n \u8fd4\u56de: (\u6700\u4f18\u7b54\u6848, \u6700\u4f18\u7f6e\u4fe1\u5ea6, \u6d88\u8017\u7684\u603b Token \u7b97\u529b)
\n """
\n best_candidate = None
\n best_score = -1.0
\n total_tokens_consumed = 0<\/p>\n # \u5728\u6d4b\u8bd5\u671f\u5e76\u884c\u91c7\u6837 N \u6761\u601d\u8003\u8def\u5f84 (\u6d88\u8017 N \u500d\u524d\u5411\u63a8\u7406\u7b97\u529b)
\n for _ in range(n_candidates):
\n curr = prompt_tokens.clone()
\n candidate_seq = []
\n for _ in range(max_new_tokens):
\n logits = self.model(curr)
\n # \u6e29\u5ea6\u91c7\u6837\u5f15\u5165\u63a2\u7d22\u6027
\n probs = F.softmax(logits[:, -1, :] \/ temperature, dim=-1)
\n next_token = torch.multinomial(probs, num_samples=1)
\n curr = torch.cat([curr, next_token], dim=1)
\n candidate_seq.append(next_token.item())<\/p>\n total_tokens_consumed += len(candidate_seq)
\n # \u9a8c\u8bc1\u5668\u8bc4\u4f30\u5f53\u524d\u601d\u8003\u8def\u5f84\u7684\u8d28\u91cf
\n score = self.simulate_verify_solution(candidate_seq)
\n if score > best_score:
\n best_score = score
\n best_candidate = candidate_seq<\/p>\n return best_candidate, best_score, total_tokens_consumed<\/p>\n if __name__ == "__main__":
\n torch.manual_seed(2026)
\n model = MockReasoningLLM()
\n evaluator = TestTimeComputeEvaluator(model)<\/p>\n prompt = torch.tensor([[10, 25, 33, 48]]) # \u6a21\u62df\u8f93\u5165 Query<\/p>\n print("=================================================================")
\n print("🔬 \u918d\u9190\u5b9e\u9a8c\u5ba4：\u5927\u6a21\u578b\u6d4b\u8bd5\u671f\u8ba1\u7b97（Test-Time Compute）\u6269\u5c55\u7269\u7406\u5b9e\u9a8c")
\n print("=================================================================\\\\n")<\/p>\n # 1. \u4f20\u7edf\u57fa\u7ebf\u6d4b\u8bd5：\u5355\u6b21\u5feb\u601d\u8003 (N=1)
\n greedy_res = evaluator.generate_single_greedy(prompt)
\n greedy_score = evaluator.simulate_verify_solution(greedy_res)
\n print(f"1. [\u4f20\u7edf\u81ea\u56de\u5f52\u8d2a\u5fc3\u751f\u6210] (N=1):")
\n print(f" – \u4ea7\u51fa\u5e8f\u5217\u7247\u6bb5: {greedy_res[:6]}…")
\n print(f" – \u9a8c\u8bc1\u5668\u5f97\u5206: {greedy_score:.4f} | \u6d88\u8017 Token: {len(greedy_res)}")<\/p>\n # 2. \u6d4b\u8bd5\u671f\u8ba1\u7b97\u4e2d\u5ea6\u6269\u5c55 (N=8)
\n res_8, score_8, tokens_8 = evaluator.generate_best_of_n(prompt, n_candidates=8)
\n print(f"\\\\n2. [\u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55 N=8] (\u4e2d\u7b49\u63a8\u7406\u7b97\u529b\u6295\u5165):")
\n print(f" – \u6700\u4f73\u89e3\u5f97\u5206: {score_8:.4f} | \u6d88\u8017 Token: {tokens_8}")<\/p>\n # 3. \u6d4b\u8bd5\u671f\u8ba1\u7b97\u91cd\u5ea6\u6269\u5c55 (N=32)
\n res_32, score_32, tokens_32 = evaluator.generate_best_of_n(prompt, n_candidates=32)
\n print(f"\\\\n3. [\u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55 N=32] (\u91cd\u5ea6\u63a8\u7406\u7b97\u529b\u6295\u5165):")
\n print(f" – \u6700\u4f73\u89e3\u5f97\u5206: {score_32:.4f} | \u6d88\u8017 Token: {tokens_32}")<\/p>\n print("\\\\n💡 \u5b9e\u9a8c\u7ed3\u8bba：\u968f\u7740\u6d4b\u8bd5\u671f\u8ba1\u7b97\u7b97\u529b（\u91c7\u6837\u8def\u5f84 N）\u7684\u6307\u6570\u589e\u52a0，\u9a8c\u8bc1\u5668\u5f97\u5206\u5b9e\u73b0\u6536\u655b\u63d0\u5347！")
\n print("=================================================================")<\/p>\n
\n\u4e94\u3001\u6d4b\u8bd5\u671f\u8ba1\u7b97\u6269\u5c55\u7684\u7269\u7406\u74f6\u9888\u4e0e\u201c\u6536\u76ca\u9012\u51cf\u201d\u7ea2\u7ebf<\/h3>\n\u5728\u4eab\u53d7\u6df1\u5ea6\u601d\u8003\u6a21\u578b\u5e26\u6765\u667a\u529b\u8dc3\u8fc1\u7684\u540c\u65f6，\u7b97\u6cd5\u67b6\u6784\u5e08\u5fc5\u987b\u6e05\u9192\u5730\u8ba4\u8bc6\u5230\u6d4b\u8bd5\u671f\u8ba1\u7b97\u7684\u4e09\u9879\u5e95\u5c42\u7269\u7406\u9650\u5236：<\/p>\n - \u201c\u8fc7\u5ea6\u601d\u8003（Overthinking）\u201d\u5f15\u53d1\u7684\u667a\u529b\u52a3\u5316\u4e0e\u7b97\u529b\u6d6a\u8d39：\u5bf9\u4e8e\u7b80\u5355\u7684\u5e38\u8bc6\u6027\u95ee\u9898（\u5982\u201c\u6cd5\u56fd\u7684\u9996\u90fd\u662f\u54ea\u91cc\u201d），\u5f3a\u884c\u8ba9\u6a21\u578b\u5c55\u5f00 5000 \u4e2a Thinking Token \u4e0d\u4ec5\u6d6a\u8d39\u663e\u5b58\u4e0e\u7535\u8d39，\u751a\u81f3\u5bb9\u6613\u5728\u5197\u957f\u7684\u601d\u7ef4\u6f2b\u6e38\u4e2d\u8bef\u5165\u6b67\u9014（\u4ea7\u751f\u65e0\u8c13\u7684\u81ea\u6211\u6000\u7591\u4e0e\u53d1\u6563\u5e7b\u89c9）\u3002**\u81ea\u9002\u5e94\u601d\u8003\u6df1\u5ea6（Adaptive Thinking Budget）**\u662f\u5fc5\u4fee\u8bfe；<\/li>\n
- \u9a8c\u8bc1\u5668（Reward Model \/ Verifier）\u7684\u201c\u88ab\u5229\u7528（Reward Hacking）\u201d\u5371\u673a：\u5728 Best-of-N \u641c\u7d22\u4e2d，\u5982\u679c\u5916\u90e8\u6253\u5206\u6a21\u578b\u7684\u5224\u522b\u51c6\u786e\u7387\u65e0\u6cd5\u8d85\u8d8a\u751f\u6210\u6a21\u578b，\u5f53\u641c\u7d22\u5019\u9009\u6570 $N > 256$ \u65f6，\u751f\u6210\u7aef\u4f1a\u4e13\u95e8\u94bb\u9a8c\u8bc1\u5668\u7684\u6253\u5206\u6f0f\u6d1e，\u4ea7\u51fa\u9ad8\u5206\u4f46\u4e8b\u5b9e\u8352\u8c2c\u7684\u6b3a\u9a97\u6027\u89e3\u7b54；<\/li>\n
- KV Cache \u663e\u5b58\u7206\u70b8\u4e0e\u957f\u5e8f\u5217 TTFT（\u9996\u5b57\u5ef6\u8fdf）\u96ea\u5d29：\u6570\u5343\u4e2a\u601d\u8003 Token \u4f1a\u8fc5\u901f\u5403\u6ee1 GPU \u663e\u5b58，\u5bfc\u81f4\u5355\u8bf7\u6c42\u7684\u663e\u5b58\u5360\u7528\u4ece 50MB \u66b4\u589e\u81f3\u6570 GB，\u6781\u5927\u538b\u7f29\u4e86\u670d\u52a1\u7aef\u7684\u5e76\u53d1\u627f\u8f7d\u80fd\u529b\u3002<\/li>\n
\u901a\u8fc7\u6df1\u523b\u7406\u89e3\u4ece\u201c\u5355\u5411\u81ea\u56de\u5f52\u5feb\u601d\u8003\u201d\u5230\u201c\u6d4b\u8bd5\u671f\u8ba1\u7b97\u9ad8\u7ef4\u7a7a\u95f4\u63a2\u7d22\u201d\u7684\u7269\u7406\u8dc3\u8fc1，\u6280\u672f\u56e2\u961f\u80fd\u591f\u5728\u6784\u5efa\u4e0b\u4e00\u4ee3\u524d\u6cbf\u63a8\u7406\u667a\u80fd\u4f53\u65f6，\u79d1\u5b66\u6743\u8861\u9884\u8bad\u7ec3\u53c2\u6570\u89c4\u6a21\u4e0e\u5728\u7ebf\u63a8\u7406\u7b97\u529b\u9884\u7b97，\u5728\u7b97\u6cd5\u80fd\u529b\u4e0e\u5546\u4e1a\u843d\u5730\u6210\u672c\u4e4b\u95f4\u627e\u5230\u6700\u4f18\u7684\u9ec4\u91d1\u652f\u70b9\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u5927\u6a21\u578b\u6df1\u5ea6\u601d\u8003\u673a\u5236\u62c6\u89e3：\u4ece\u81ea\u56de\u5f52 Next-Token \u5230\u6d4b\u8bd5\u671f\u8ba1\u7b97（Test-Time Compute）\u7269\u7406\u8fb9\u754c
\n\u5728\u8fc7\u53bb\u7684\u51e0\u5e74\u91cc，\u6574\u4e2a\u6df1\u5ea6\u5b66\u4e60\u4e0e\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u5de5\u4e1a\u754c\u51e0\u4e4e\u5168\u76d8\u62bc\u6ce8\u5728 \u201c\u9884\u8bad\u7ec3\u6269\u5c55\u5b9a\u5f8b（Pre-training Scaling Law）\u201d \u4e0a：\u901a\u8fc7\u5806\u53e0\u6570\u5341\u4e07\u5757 GPU\u3001\u6295\u5582\u6570\u5341\u4e07\u4ebf Token \u7684\u65e0\u6807\u6ce8\u4e92\u8054\u7f51\u8bed\u6599，\u5c06\u5bc6<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[11044,50],"topic":[],"class_list":["post-96486","post","type-post","status-publish","format-standard","hentry","category-server","tag-next-token","tag-50"],"yoast_head":"\n \u5927\u6a21\u578b\u6df1\u5ea6\u601d\u8003\u673a\u5236\u62c6\u89e3\uff1a\u4ece\u81ea\u56de\u5f52 Next-Token \u5230\u6d4b\u8bd5\u671f\u8ba1\u7b97\uff08Test-Time Compute\uff09\u7269\u7406\u8fb9\u754c - 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