42 lines
1.2 KiB
Markdown
42 lines
1.2 KiB
Markdown
---
|
||
title: "Stochastic Latent Trajectory（随机潜在轨迹）"
|
||
created: 2026-05-23
|
||
updated: 2026-05-23
|
||
type: concept
|
||
tags: [reasoning, stochastic, latent, trajectory]
|
||
sources: [raw/papers/gram-generative-recursive-reasoning-2026.md]
|
||
confidence: high
|
||
---
|
||
|
||
# Stochastic Latent Trajectory
|
||
|
||
> GRAM 的核心创新：将推理过程建模为**随机潜在轨迹**，每次递归步从分布中采样下一步状态，而非确定性更新。
|
||
|
||
## 形式化
|
||
|
||
给定输入 x 和前一步潜在状态 z_{t-1}：
|
||
|
||
z_t ~ p_theta(z_t | z_{t-1}, e_x)
|
||
|
||
T 步后得到轨迹 (z_0, z_1, ..., z_T)，最终预测由解码器从 z_T 产生。
|
||
|
||
## 关键区别
|
||
|
||
| | 确定性 RRM | GRAM (随机) |
|
||
|---|----------|------------|
|
||
| 转移 | z_t = f(z_{t-1}, e_x) | z_t ~ p(z_t | z_{t-1}, e_x) |
|
||
| 轨迹数 | 1 条 | 分布上的多条 |
|
||
| 预测 | 单点 | 边际化 |
|
||
|
||
## 为什么需要随机性
|
||
|
||
- 维持**不确定性**：不确定的区域保留多条路径
|
||
- 探索**替代策略**：不同轨迹探索不同解空间
|
||
- 实现**[[inference-time-scaling|推理时扩展]]**：通过并行采样轨迹 scale
|
||
|
||
## 相关概念
|
||
|
||
- [[gram-generative-recursive-reasoning|GRAM]]
|
||
- [[multi-trajectory-inference]]
|
||
- [[deep-and-wide-reasoning]]
|