RLM paper: GPT-5-mini-based RLM doubles GPT-5 accuracy on OOLONG, handles 10M+ tokens
gordic_aleksa · x · 2026-09-20
The Recursive Language Models (RLM) paper (paper, code) proposes an inference strategy where LMs treat their input context as a variable in a Python REPL, recursively decomposing and interacting with it to handle essentially unbounded context and mitigate context rot.
Key results:
- On OOLONG, the hardest long-context benchmark they found, an RLM using GPT-5-mini more than doubles GPT-5's correct answers at lower average cost per query
- On a new long-context Deep Research task built from BrowseComp-Plus, RLMs beat ReAct + test-time indexing/retrieval
- Surprisingly, performance doesn't degrade at 10M+ tokens
The authors argue RLMs explicitly trained to reason recursively may be the next milestone in inference-time scaling after CoT and ReAct-style agents.
Related event: Recursive Language Models Let LMs Call Themselves on Long Contexts(2 posts)→
More from Research
- Anthropic says it may imminently hit RSP threshold for AI-accelerated R&D, releases provable inference work — tobyordoxford · 2026-11-04
- Google scanned 1,033 real household products, packed into a 345 MB GLB for three.js — victormustar · 2026-11-04
- These 10 GPU optimization papers are why your model runs fast today — NandoDF · 2026-11-04
- INT8 isn't the problem: I benchmarked samplers on INT8 flow models — YakovSava · 2026-11-04
- Karpathy Boosts Open Hacker News Dataset: 10M+ Posts Usable as a Personal Knowledge Base — huangyun_122 · 2026-11-04
- PixelCraft: A Multi-Agent Framework Targets Multimodal AI's Chart-Reading Failures — burkov · 2026-11-04