posted in Technology

eicker@eicker@lemmy.world

Alibaba’s AI model Qwen3.8-Max made widely accessible ahead of open-weights release: Flagship 2.4 trillion parameter model highlights native multimodal capabilities and ... agentic tasks.

www.scmp.com/tech/article/3362738/alibabas-ai-model-qwen38-max-made-widely-accessible-ahead-open-weights-release
South China Morning PostAlibaba’s AI model Qwen3.8-Max widely accessible ahead of open-weights releaseFlagship 2.4 trillion parameter model highlights native multimodal capabilities and the ability to carry out long-horizon agentic tasks.
Meron35@Meron35@lemmy.world

Replying to @⁨PetteriPano@lemmy.world⁩

Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.

The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.

No, Chinese Is Not More Token-Efficient Than English for LLMs | markhuang.ai - markhuang.ai/blog/chinese-token-myth

markhuang.aimarkhuang.aiNo, Chinese Is Not More Token-Efficient Than English for LLMsA native Mandarin speaker tests the popular claim that Chinese characters save tokens when interacting with LLMs. Across six tokenizers — including Chinese-first models like Qwen, GLM, and DeepSeek — English uses fewer tokens every time. The data, the BPE mechanics, and why character count has nothing to do with token count.
PetteriPano@PetteriPano@lemmy.world

Replying to @⁨Meron35@lemmy.world⁩

I didn’t say it’s more token efficient. I said training multiple languages improves reasoning.

arXiv logoarXiv.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 comprehensive
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