LLM use is like MCMC: reward-seeking prompts that never backtrack
rickasaurus · x · 2026-08-25
@TricklerHQ argues that current LLMs and how we use them resemble a reward-seeking stochastic process, like MCMC—each incremental prompt climbs toward a higher energy state without ever backtracking. A reply pushes back: if models were that dumb they couldn't handle long-running tasks, and argues LLMs will always need a harness providing "sense organs" and capabilities; all the nudging and rewriting just reflects that models are still bad.
Related event: View: LLMs Are Non-Backtracking MCMC and Need a Harness(3 posts)→
More from Models
- Same base model, 6x agentic jump: GLM-5.3 hits 28.3% on Terminal-Bench via post-training — bittingthembits · 2026-08-25
- Traders dump 2600% swing on Gemini downloads bet; mystery 0x Alpha model stirs markets — adrianscottcom · 2026-08-25
- Anjney Midha: Two Unreleased Frontier AI Models Showed 'Different' Security Characteristics — daniel_mac8 · 2026-08-25
- $40 experiment: Opus 5 hits ~0.94 F1 on ExtractBench, Qwen3.8 matches at 1/3 the price — Ok-Challenge-7810 · 2026-08-25
- Claude vs. Grok: Restrictive safety vs. clever workarounds — whatsallthiss · 2026-08-25
- 165 GPU Hours Testing 12 Abliterated Gemma 4 12B Variants: The Most Jailbroken One Destabilizes Reasoning — nathandreamfast · 2026-08-25