Replying to a post on startrek.website
Technically, “the Holy Spirit” isn’t a proper noun.
That’s the spirit.
Replying to a post on startrek.website
Technically, “the Holy Spirit” isn’t a proper noun.
That’s the spirit.
Replying to @Redacted@lemmy.world
I don’t like to spend my days on reddit, so I have my Hermes agent scrape the subreddits I want news from to get a digest. Today it started failing. So my Hermes agent got the credentials to a throwaway and carried on like usual.
Replying to @return2ozma@lemmy.world
I bought a house ten years ago. I’ve locked the mortgage payments at ~$1000. I’ve since moved countries and been renting it out for the last four years. The current tenants pay ~$2200 and their own utilities. Seven more years and my mortgage is no more.
Replying to @BackgrndNoize@lemmy.world
Your data is living on borrowed time as it is if you don’t have it backed up already.
Replying to @Meron35@lemmy.world
I didn’t say it’s more token efficient. I said training multiple languages improves reasoning.
arXiv.orgBeyond English-Centric Training: How Reinforcement Learning Improves Cross-Lingual Reasoning in LLMsEnhancing the complex reasoning capabilities of Large Language Models (LLMs) attracts widespread attention. While reinforcement learning (RL) has shown superior performance for improving complex reasoning, its impact on cross-lingual generalization compared to Supervised Fine-Tuning (SFT) remains unexplored. We present the first systematic investigation into cross-lingual reasoning generalization of RL and SFT. Using Qwen2.5-3B-Base as our foundation model, we conduct experiments on diverse multilingual reasoning benchmarks, including math reasoning, commonsense reasoning, and scientific reasoning. Our investigation yields two significant findings: (1) Tuning with RL not only achieves higher accuracy but also demonstrates substantially stronger cross-lingual generalization capabilities compared to SFT. (2) RL training on non-English data yields better overall performance and generalization than training on English data, which is not observed with SFT. Furthermore, through comprehensiveReplying to @VonReposti@feddit.dk
random Chinese character
It’s beneficial for reasoning to have models trained in a few languages. Chinese is a good one because one character is one word is one token.