Seeking collaborators for scaling and independent evaluation of a new recurrent language model architecture (preprint + code) [R]
Summary
The author is seeking collaborators for scaling and independent evaluation of a new recurrent language model architecture, with a preprint and code available.
Similar Articles
(Towards) Scalable Reliable Automated Evaluation with Large Language Models
This paper proposes a scalable, domain-agnostic framework for automated LLM evaluation that uses pairwise comparisons by multiple LLMs and an Elo rating system to approximate expert judgments, reducing the need for human intervention.
Reinforcing Recursive Language Models (18 minute read)
The article explores reinforcement learning fine-tuning of small (4B) recursive language models (RLMs) to perform evidence selection from scientific documents, showing that RL-trained 4B models match Claude Sonnet 4.6 performance at a fraction of the size and cost.
Models for minimalist RAG: B1ade 335M Embedding and 1B Parameter Small Language Models
Presents B1ade, a minimalist RAG architecture with a 335M zero-training embedding model and a 1B SLM trained via GRPO on 723M tokens, showing emergent attribution behavior and competitive QA performance without large-scale pretraining.
@Recursive_SI: https://x.com/Recursive_SI/status/2064980090702962699
Recursive releases early results from its automated AI research system, achieving state-of-the-art in fixed-budget language model training, small-model training speed, and GPU kernel optimization, and open-sources artifacts.
alexzhang13/rlm
Recursive Language Models (RLMs) introduce a task-agnostic inference paradigm enabling language models to handle near-infinite contexts by recursively calling themselves over input, with an accompanying open-source inference engine and training environment.