Qwen3
qwen3-e6bbc535·20 events·first seen 1mo agoAliases: Qwen 3, Qwen-3, Qwen3, Qwen3.6, Qwen3.5, Qwen3.7
Merged from
Qwen 3, Qwen-3
Co-occurring entities
More like this (12)
Recent events (20)
Alibaba releases Qwen3.5 open-weights vision-language model family with MoE architecture across eight sizes
Alibaba released the Qwen3.5 family of eight open-weights vision-language models ranging from 0.8B to 397B parameters, built on a mixture-of-experts architecture with mixed attention and Gated DeltaNet layers. The flagship Qwen3.5-397B-A17B outperforms GPT-5.2, Claude 4.5 Opus, and Gemini-3 Pro on 28 of 44 vision benchmarks, while the 9B model surpasses OpenAI's gpt-oss-120B on most language tasks. Open weights are available under Apache 2.0, with hosted agentic variants (Qwen3.5-Plus, Qwen3.5-Flash) available via Alibaba Cloud. The release is notable for strong small-model efficiency and comes amid reported team departures following the Qwen3 rollout.
The 4 Things Qwen-3's Chat Template Teaches Us
A Hugging Face blog post performs a deep dive into the chat template design of Qwen-3, examining the technical choices made in its prompt formatting and conversation structure. The analysis surfaces lessons about how chat templates encode model behavior, reasoning modes, and tool-use conventions. As a tier-2 commentary piece, it provides practical implementation guidance for developers integrating Qwen-3 into applications.
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
EnvFactory is a fully automated framework for training tool-use LLM agents via Agentic Reinforcement Learning, addressing two key bottlenecks: scalable execution environments and realistic multi-turn training data. It autonomously constructs stateful, executable tool environments from authentic resources and synthesizes natural trajectories with implicit human intents via topology-aware sampling. Using only 85 verified environments across 7 domains, it generates 2,575 SFT and RL trajectories and improves Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks, outperforming prior approaches that use 5x more environments.
ZPPO: Teacher-in-prompt training method outperforms distillation and GRPO for small vision-language models
Researchers introduce Zone of Proximal Policy Optimization (ZPPO), a training method inspired by Vygotsky's zone of proximal development that embeds teacher guidance in prompts rather than policy gradients or logit imitation. On hard questions where student rollouts fail, ZPPO constructs Binary Candidate-included Questions (BCQ) and Negative Candidate-included Questions (NCQ) to help the student discriminate correct from incorrect responses, with a replay buffer that recirculates hard questions until mastered. Evaluated on the Qwen3 family (0.8B–9B) with a 27B teacher across a 31-benchmark suite covering VLM, LLM, and video tasks, ZPPO outperforms both distillation and GRPO baselines, with the largest gains at the smallest model scale. The method addresses a known failure mode of RL training where zero-reward rollouts produce no gradient signal.
Study finds thinking mode in LRMs shifts instruction-following errors by constraint type rather than uniformly degrading performance
A new arXiv paper investigates how enabling built-in chain-of-thought reasoning ('Thinking ON/OFF') in Qwen3 and Hunyuan models affects instruction following on IFEval. Aggregate pass-rate changes are small but 10-20% of prompts switch outcomes, with 'Planning' constraints (global counting, structure) improving under thinking while 'Precision' constraints (exact local form) consistently worsen. Activation patching and trace-relevance analyses reveal an execution gap: thinking traces engage with Planning constraints but fail to translate that engagement into compliance, while Precision failures are more mechanistically recoverable. The findings have practical implications for when to enable reasoning modes in instruction-following applications.
Qwen3Guard: Real-time Safety Guardrail Model for Token Stream Classification
Alibaba's Qwen team has released Qwen3Guard, the first dedicated safety guardrail model in the Qwen family, built on Qwen3 foundation models and fine-tuned for safety classification. The model performs real-time safety detection on both prompts and responses, providing risk levels and categorized classifications for content moderation. Qwen3Guard claims state-of-the-art performance on major safety benchmarks across English, Chinese, and multilingual settings.
Qwen-MT Turbo: Alibaba Releases Specialized Translation Model Supporting 92 Languages
Alibaba's Qwen team has released qwen-mt-turbo, a specialized machine translation model built on Qwen3 and trained on trillions of multilingual and translation tokens. The model supports 92 languages and dialects covering over 95% of the global population. It incorporates reinforcement learning techniques to improve translation accuracy and linguistic fluency, and is available via the Qwen API.
Qwen3 Embedding: State-of-the-Art Text Embedding and Reranking Models Released
Alibaba's Qwen team has released the Qwen3 Embedding series, a set of open-weights text embedding and reranking models built on the Qwen3 foundation model. The models are designed for retrieval and reranking tasks and claim state-of-the-art performance across multiple benchmarks. They are released under the Apache 2.0 license and are available on Hugging Face and ModelScope.
The Batch Issue 345: Iranian Drone Attacks on AWS Data Centers, Qwen3.5, DeepSeek-Huawei, and AI Job Insecurity
Andrew Ng's weekly newsletter covers several significant AI-adjacent developments: Iranian drones struck at least three Amazon Web Services data centers in Bahrain and the UAE, disrupting cloud services and raising concerns given U.S. military use of AWS to run Anthropic Claude; the issue also previews Qwen3.5 model releases across multiple sizes and DeepSeek's reported moves involving Huawei hardware. Ng also addresses widespread job insecurity across skill levels amid rapid AI advancement, citing geopolitical risks including the Iran war, Taiwan uncertainty, and rare-earth metal supply chains as compounding factors.
VaSE: Value-Aware Stochastic KV Cache Eviction improves reasoning model efficiency
A new arXiv preprint introduces Value-aware Stochastic KV Cache Eviction (VaSE), a training-free method for compressing KV caches in long-chain-of-thought reasoning models. The authors identify two key failure modes in prior eviction approaches — catastrophic repetition loops caused by evicting high-magnitude value states, and low cache diversity — and address both with targeted protections and stochastic eviction. On six reasoning tasks with Qwen3 models at 4x compression, VaSE outperforms the current best selection-based sparse attention method and exceeds the strongest eviction baseline by over 4%, while supporting FlashAttention2 and maintaining a static memory footprint.
Qwen 3.7 Preview Announced by Alibaba
Alibaba's Qwen team has announced a preview of Qwen 3.7, the next iteration in their Qwen 3 model series. The announcement appeared on Twitter/X and generated notable community discussion on Hacker News with 179 points and 67 comments. Specific capability details and model specifications are not available from this source alone.
SCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks
SCOPE is a data-free self-play framework for training language models on open-ended tasks without external supervision or frontier-model judges. It co-evolves two policies—a Challenger that generates document-grounded tasks and a Solver that answers via multi-turn retrieval—using a frozen copy of the initial model as a self-judge that writes task-specific rubrics. Across three 7-8B models (Qwen2.5, Qwen3, OLMo-3), SCOPE achieves up to +10.4 points on eight open-ended benchmarks and +13.8 points on seven held-out short-form QA benchmarks, matching or exceeding GRPO trained on ~9K curated prompts. Ablations identify rubric generation quality as the primary bottleneck for self-judging.
LLMs predict dementia and depression severity from clinical interview transcripts in zero-shot and feature-extraction settings
Researchers evaluate three open-weights LLMs (Mistral 3.1, DeepHermes, Qwen3) for predicting dementia and depression severity from speech transcripts of 154 German-speaking patients in standardized clinical interviews. The study introduces a new observer-based Global Depression Scale (GDS-D) and tests both zero-shot prediction and LLM-based feature extraction for Support Vector Regression. Zero-shot performs well for depression (MAE 0.60), while structured feature extraction reduces dementia assessment error by up to 35%; pause-enriched automatic transcripts match human transcription quality, suggesting viable fully-automated screening pipelines.
Bebop: MTP with rejection sampling and TV loss achieves 1.8x RL training speedup
Researchers introduce Bebop, a framework for integrating Multi-Token Prediction (MTP) into large-scale RL training pipelines for LLMs. The work identifies that MTP acceptance rates degrade during RL due to entropy fluctuations, and proposes probabilistic rejection sampling plus a novel end-to-end Total Variation (TV) loss that directly optimizes multi-step acceptance rates, achieving up to 95% acceptance rates and 25% extra inference throughput gains. Applied to Qwen3.5, Qwen3.6, and Qwen3.7 models, the method yields up to 1.8x end-to-end acceleration in async RL training. The approach eliminates the need for costly online MTP updating by using pre-RL MTP training with the proposed objectives.
OpenPipe ART: Agent Reinforcement Trainer for Multi-Step Agents via GRPO
OpenPipe has released ART (Agent Reinforcement Trainer), an open-source Python library for training multi-step agents on real-world tasks using GRPO (Group Relative Policy Optimization). The framework supports multiple model families including Qwen3, GPT-OSS, and Llama. With nearly 10k GitHub stars and 66 gained today, it is gaining notable community traction as a practical RL fine-tuning tool for agentic workflows.
Expert Tying reduces MoE LLM memory footprint by ~2x with minimal quality loss
Researchers introduce Expert Tying, an architectural modification for Mixture-of-Experts LLMs that shares expert parameters across consecutive transformer layers while keeping routing and attention layer-independent. Evaluated on OLMoE, Qwen3, and DeepSeek-style MoE architectures, the method achieves nearly 2x memory reduction with negligible perplexity or downstream quality degradation. The approach exploits parameter redundancy in MoE pathways to improve the compute-to-memory trade-off for training and inference.
DeepSeek releases R1-0528-Qwen3-8B distilled reasoning model on Hugging Face
DeepSeek released DeepSeek-R1-0528-Qwen3-8B, an 8B parameter text-generation model on Hugging Face, combining the R1-0528 reasoning capabilities with a Qwen3 base. The model has accumulated over 306K downloads and 1K likes shortly after release, indicating strong community uptake. This appears to be a distilled version of the R1-0528 reasoning model targeting smaller-scale deployment.
Unsloth: Web UI and Library for Efficient Fine-tuning of Open Models
Unsloth is an open-source Python library and web UI (Unsloth Studio) for efficient fine-tuning and local inference of open-weight models including Gemma 4, Qwen3, DeepSeek, and GPT-OSS variants. The project has accumulated over 64,000 GitHub stars with continued daily growth (+139 today), indicating strong community adoption. It targets practitioners who want to train and run large models locally with reduced memory and compute requirements.
Data Points: OpenAI and Microsoft sever their exclusive relationship
This edition of The Batch covers several major AI industry developments: OpenAI has revised its partnership with Microsoft, ending exclusivity while retaining Microsoft as primary cloud partner through 2032 and gaining freedom to deploy on AWS and Google Cloud. DeepSeek released V4 model weights featuring 1M-token context and Huawei Ascend chip optimization, though it trails leading open and closed models on aggregate benchmarks. Google and Amazon are deepening investments in Anthropic with up to $40B and $25B respectively in funding-for-compute deals, and an agentic AI system autonomously designed a functional RISC-V CPU from a 219-word spec in 12 hours.
ms-swift: ModelScope framework for fine-tuning 600+ LLMs and 300+ MLLMs
ms-swift is an open-source Python framework from ModelScope supporting PEFT and full-parameter fine-tuning methods (CPT, SFT, DPO, GRPO) across 600+ LLMs and 300+ multimodal LLMs, including Qwen3, DeepSeek, Llama4, and others. The project has accumulated 14,487 GitHub stars and was accepted at AAAI 2025. It serves as a broad-coverage training harness for the current generation of open-weights frontier models.