Unverified50% confidenceOpinionExact time
大模型能在逆向环节表现出色,根本原因在于预训练对海量人类编写代码进行统计建模,具备识别代码模式而非仅字面的能力
1
Sources
50%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
Unverified大模型本质上是token生成器,训练目标是生成看起来自然的文本,而非精确的数据结构77% similarUnverifiedLarge language models are optimized to make code look correct rather than to meet current-stage requirements; they are fundamentally probability-statistical machines that predict the next most likely token and excel at reproducing high-frequency patterns from training data.76% similarVerifiedPre-training on code not only improves performance on programming tasks but also significantly enhances a model's logical reasoning capabilities, and has become standard practice for top-tier foundation models.76% similarUnverifiedSupervised fine-tuning trains models to imitate correct answers labeled by humans, while reinforcement learning fine-tuning allows models to learn optimal strategies through trial and error.76% similarVerifiedLarge language models do not store raw text in the traditional sense during training; instead, they encode statistical patterns into billions of parameters through the backpropagation algorithm.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/556445API
curl https://kongchang.com/api/v1/knowledge/claims/556445MCP
get_claim(id=556445)