# Boltzbit 发布《Infinite-Parameter LLMs》论文预览版，提出 BAST

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-09-25 03:25
- AIHOT 分数：36
- AIHOT 链接：https://aihot.news/items/cmufxsh9704e0rogv2z3b7tj6
- 原文链接：https://x.com/rohanpaul_ai/status/2103204263346438480

## AI 摘要

Boltzbit 发布论文预览版《Infinite-Parameter LLMs》，提出 Bayesian Self-learning Transformers（BAST），将实时数据转化为定向参数更新。其理论分析估算，BAST 的算力需求比常规训练低约 1,000 倍。团队称 BAST 在长上下文对话中于成本和性能上均克服了 RAG 等基于记忆的学习方式的局限。

## 正文

There are two ways to make an agent improve.

One is to leave the model unchanged and accumulate documents, embeddings, summaries, skills, and longer histories.

The other is to let experience modify the model.

Boltzbit’s Bayesian Self-learning Transformers (BAST) investigate the second path by converting live data into targeted parameter updates. Its theoretical analysis estimates roughly 1,000× lower compute requirements than conventional training.

The economics matter. External memory makes future requests retrieve and process more state. Weight adaptation moves useful knowledge into the model itself.

The next benchmark is whether those updates remain stable, selective, and generalize beyond the interaction that produced them.

If they do, continual learning becomes part of the architecture, not another agent feature.

### 引用推文

> Boltzbit：We’ve just released the preview version of our latest paper: Infinite-Parameter LLMs — Generating and Adapting Weights from Live Data. In it, we demonstrate tha...