The Sequence Radar #715: Qwen-Max: The Trillion-Parameter MoE You Can Actually Ship

One of the most impressive releases of the generative AI era.

Created Using GPT-5

Next Week in The Sequence:

1. Knowledge: We dive into Circuits and its role in mechanistic interpretability.

2. AI of the Week : We are going deep into Qwen-Max.

3. Opinion: We discuss the current transition from pretraining to post-training.

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📝 Editorial: The Trillion-Parameter MoE You Can Actually Ship

Alibaba Qwen just released one of the most impressive AI models ever created.

Qwen‑Max was introduced as the flagship tier of the Qwen 2.5 lineup from Alibaba Cloud, rolled out through DashScope/Model Studio with an OpenAI‑compatible endpoint. The launch message was straightforward: bring a frontier‑class Mixture‑of‑Experts (MoE) model to production developers with minimal integration friction, highlight strengths in math/coding and long‑form reasoning, and pair the managed “Max” service with open‑weight and multimodal siblings so teams can choose the right deployment style for each workload. While it isn’t the first trillion‑parameter model—research MoEs crossed that line years ago—it’s the first trillion‑scale entry publicly positioned as a flagship among the major production chat stacks.

Qwen‑Max is Alibaba Cloud’s flagship MoE language model, delivered through an OpenAI‑compatible API. In practice, that means you can point your existing Chat Completions client at a new base URL and get frontier‑class behavior—no SDK rewrites. The contribution that matters most here is pragmatic accessibility: Qwen‑Max packages extreme‑scale training and modern alignment into an interface developers already know, lowering the friction to evaluate and deploy a top‑tier model.

Under the hood, MoE gives Qwen‑Max high capacity without paying the full dense‑model cost for every token. A router activates a small subset of specialized “experts” per token, concentrating compute where it’s useful and skipping the rest. The tricky part with MoE is stability—avoiding collapsed routing, underused experts, or training instabilities. Qwen‑Max’s recipe (large‑scale pretraining followed by staged SFT and RLHF) shows that you can keep experts well‑utilized and instruction following strong, making sparse models dependable enough for production.

On capability, Qwen‑Max performs especially well on math, code, and hard multi‑step prompts—the stuff that actually blocks teams in daily workflows. It handles long‑form reasoning, tool use, and structured outputs with fewer derailments, which translates to less prompt‑engineering contortion and fewer fallbacks. For engineering teams, that combination—reasoning quality plus reliability—often matters more than leaderboard bragging rights because it shows up as higher task completion rates and lower human‑in‑the‑loop load.

A second, underappreciated contribution is the surrounding ecosystem. The Qwen family spans open‑weight models for on‑prem customization, multimodal variants for vision+language, and long‑context options for document‑heavy retrieval. That spectrum lets you mix and match: keep open models where data governance or latency demands it, and call Qwen‑Max in the cloud when you need peak accuracy on the hardest tasks. It’s a practical template for regulated environments that still want access to frontier‑level capability.

Operationally, Qwen‑Max is easy to slot into modern stacks. API compatibility enables quick A/B tests behind a router, so you can pit it against incumbents using your own eval harness and decide on the basis of latency × quality × cost. MoE’s sparsity further improves cost‑per‑useful‑token at a given quality target, which is what matters to finance, analytics, and dev‑assist workloads that are both compute‑intensive and quality‑sensitive. The roadmap also signals continued pressure at the high end (larger MoE, longer context windows) without abandoning ergonomics. That pace of iteration is itself a contribution: it suggests we don’t have to choose between scale, alignment, and developer experience. For teams deciding when to try it: if your bottlenecks are reasoning‑heavy tasks (complex coding, data analysis, policy‑aware generation) and you value drop‑in integration, Qwen‑Max is a compelling candidate to run through your internal evals.

🔎 AI Research

Open Data Synthesis for Deep Research

AI Lab: BAAI
Summary: This paper introduces InfoSeek, a framework that generates large-scale Deep Research datasets by formalizing questions as Hierarchical Constraint Satisfaction Problems (HCSPs), requiring layered, interdependent reasoning steps. The resulting dataset (50K+ samples) significantly boosts LLM performance on complex search and reasoning benchmarks like BrowseComp-Plus, enabling compact models (3B) to rival much larger or commercial systems2509.00375v1.

Jointly Reinforcing Diversity and Quality in Language Model Generations

AI Lab: Meta FAIR, Carnegie Mellon University, Johns Hopkins University
Summary: The authors present Darling (Diversity-Aware Reinforcement Learning), which combines a learned semantic diversity classifier with quality rewards to encourage LLMs to generate outputs that are both high-quality and novel. Experiments across creative writing and competition math benchmarks show Darling avoids diversity collapse during post-training and improves both quality and exploration compared to GRPO and other baselines2509.02534v1 (1).

SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning

AI Lab: Nanyang Technological University, TikTok
Summary: This work proposes SimpleTIR, a plug-and-play RL algorithm that stabilizes multi-turn tool-integrated reasoning by filtering out “void turns” (responses with neither code nor answers), which otherwise cause gradient explosions. On math benchmarks like AIME24, SimpleTIR substantially outperforms prior multi-turn training methods, while encouraging diverse reasoning strategies such as cross-validation and error correction2509.02479v2.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

AI Lab: National University of Singapore, University of Oxford, Shanghai AI Lab, UCL, UIUC, Brown, Imperial College, CAS, CUHK, Fudan, Bristol, Georgia, UCSD, UCSB, Dalian Univ. of Tech
Summary: This survey synthesizes over 500 recent works on Agentic Reinforcement Learning (Agentic RL), framing LLMs as autonomous agents with capabilities such as planning, memory, tool use, reasoning, and self-improvement. It introduces a two-part taxonomy (capabilities vs. task domains), reviews open-source environments and frameworks, and highlights challenges like trustworthiness, scaling training, and environment complexity.

Towards a Unified View of Large Language Model Post-Training

AI Lab: Tsinghua University, Shanghai AI Lab, WeChat AI
Summary: This paper introduces the Unified Policy Gradient Estimator (UPGE), a theoretical framework showing that Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are not contradictory but can be expressed as instances of a single gradient formulation. Building on this, the authors propose Hybrid Post-Training (HPT), which dynamically balances SFT for exploitation and RL for exploration based on model performance, achieving consistent improvements over strong baselines on multiple mathematical reasoning benchmarks.

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers

AI Lab: CAMEL-AI.org
Summary: This paper introduces the Loong Project, consisting of LOONGBENCH (a seed dataset of 8,729 human-vetted examples across 12 reasoning-intensive domains with executable code) and LOONGENV (a modular synthetic data generation environment). Together, they enable scalable reinforcement learning with verifiable rewards (RLVR), benchmarking open- and closed-source LLMs, and generating diverse, difficult, and semantically verified reasoning tasks across domains like advanced math, chemistry, logic, and finance

🤖 AI Tech Releases

Qwen-Max-Preview

Alibaba just released Qwen-Max-Preview, a massive 1 trillion parameters model.

EmbeddingGemma

Google released EmbeddingGemma, a new open sourced embedding model with state of the art performance.

Le Chat MCP Connectors

Mistral released a new set of MCP connectors in its Le Chat platform.

📡AI Radar

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• Sierra scores a $350 million round at a $10 billion valuation
  Bret Taylor’s startup Sierra, which builds AI customer-service agents, raised $350 million—bringing its valuation to $10 billion after only 18 months in operation.

• AI logistics startup Augment lands $85 million Series A
  Augment, founded by Deliverr’s former CEO, raised an $85 million Series A to expand its AI assistant “Augie,” which automates freight logistics tasks like bid gathering and invoice handling.

• Atlassian snaps up The Browser Company for $610 million
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• Mistral nears $14 billion valuation on €2 billion funding
  French AI startup Mistral is reportedly closing in on a €2 billion investment that would value the company at around $14 billion, elevating it among Europe’s top-valued tech firms.

• Scale AI sues ex-sales rep and rival Mercor for trade-secret theft
  Scale AI filed a lawsuit alleging a former employee and competing startup Mercor tried to steal its biggest customers by misappropriating over 100 confidential documents.

• CoreWeave acquires reinforcement-learning specialist OpenPipe
  CoreWeave, a cloud provider for AI model training, acquired OpenPipe—known for its agent-training toolkit—to expand its offerings in reinforcement learning.

• Human Behavior raise $5 million to decode user behavior via vision AI
  A 20- and 22-year-old founding team launched Human Behavior, using vision AI to analyze user session replays, and quickly raised $5 million in seed funding from YC and General Catalyst.

• Anthropic lands $13 billion Series F at $183 billion valuation
  Anthropic closed a $13 billion Series F funding round, valuing the AI company at $183 billion, with plans to scale enterprise usage, safety research, and global operations.

• China’s DeepSeek prepping AI agent to rival OpenAI by year-end
  DeepSeek, based in Hangzhou, is developing an AI agent capable of multi-step tasks and autonomous learning, aimed at competing with firms like OpenAI before the end of 2025.

• Alex team joins OpenAI’s Codex in strategic acqui-hire
  OpenAI has acqui-hired the creators of Alex Codes—Xcode's AI coding assistant—and integrated the team into its Codex division.