Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing

Chen Wu, Yin Song
5/13/2025
cs.CLcs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

We present MegaBeam-Mistral-7B, a language model that supports 512K-token context length. Our work addresses practical limitations in long-context training, supporting real-world tasks such as compliance monitoring and verification. Evaluated on three long-context benchmarks, our 7B-parameter model demonstrates superior in-context learning performance on HELMET and robust retrieval and tracing capability on RULER. It is currently the only open model to achieve competitive long-range reasoning on BABILong at 512K context length without RAG or targeted fine-tuning. Released as fully open source under the Apache 2.0 license, the model has been downloaded over 100,000 times on Hugging Face. Model available at: https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models

89% match2025

How to Train Long-Context Language Models (Effectively)

87% match1 repo2622024

LongSkywork: A Training Recipe for Efficiently Extending Context Length in Large Language Models

87% match2024

Data Engineering for Scaling Language Models to 128K Context

86% match1 repo4982026

Beyond Length: Quantifying Long-Range Information for Long-Context LLM Pretraining Data

86% match2025

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

86% match2 repos2.7k2026

Cite this paper

@article{wu2025scaling,
  title  = {Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing},
  author = {Chen Wu and Yin Song},
  year   = {2025},
  eprint = {2505.08651},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.08651v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2505.08651.svg)](https://codeofpaper.com/paper/2505.08651)

Discussion