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Alibaba Releases Tongyi DeepResearch: A 30B-Parameter Open-Source Agentic LLM Optimized for Long-Horizon Research

ByRicardo

Table of contents

Alibaba’s Tongyi Lab has open-sourced Tongyi-DeepResearch-30B-A3B, an agent-specialized massive language mannequin constructed for long-horizon, deep information-seeking with internet instruments. The mannequin makes use of a mixture-of-experts (MoE) design with ~30.5B complete parameters and ~3–3.3B energetic per token, enabling excessive throughput whereas preserving sturdy reasoning efficiency. It targets multi-turn analysis workflows—looking, shopping, extracting, cross-checking, and synthesizing proof—underneath ReAct-style device use and a heavier test-time scaling mode. The launch contains weights (Apache-2.0), inference scripts, and analysis utilities.

What the benchmarks present?

Tongyi DeepResearch reviews state-of-the-art outcomes on agentic search suites steadily used to check “deep analysis” brokers:

  • Humanity’s Last Exam (HLE): 32.9,
  • BrowseComp: 43.4 (EN) and 46.7 (ZH),
  • xbench-DeepSearch: 75,
    with further sturdy outcomes throughout WebWalkerQA, GAIA, FRAMES, and SimpleQA. The workforce finds the system as on par with OpenAI-style deep analysis brokers and “systematically outperforming current proprietary and open-source” brokers throughout these duties.
https://github.com/Alibaba-NLP/DeepResearch?tab=readme-ov-file

Architecture and inference profile

  • MoE routing (Qwen3-MoE lineage) with ≈30.5B complete / ≈3.3B energetic parameters, giving the price envelope of a small dense mannequin whereas retaining specialist capability.
  • Context size: 128K tokens, appropriate for lengthy, tool-augmented shopping periods and iterative synthesis.
  • Dual inference modes:
    • ReAct (native) for direct analysis of intrinsic reasoning and gear use,
    • IterResearch “Heavy” mode for test-time scaling with structured multi-round synthesis/reconstruction of context to cut back noise accumulation.

Training pipeline: artificial information + on-policy RL

Tongyi DeepResearch is skilled end-to-end as an agent, not only a chat LLM, utilizing a totally automated, scalable information engine:

  • Agentic continuous pre-training (CPT): large-scale artificial trajectories constructed from curated corpora, historic device traces, and graph-structured data to show retrieval, shopping, and multi-source fusion.
  • Agentic SFT cold-start: trajectories in ReAct and IterResearch codecs for schema-consistent planning and gear use.
  • On-policy RL with Group Relative Policy Optimization (GRPO), token-level coverage gradients, leave-one-out benefit estimation, and negative-sample filtering to stabilize studying in non-stationary internet environments.

Role in doc and internet analysis workflows

Deep-research duties stress 4 capabilities: (1) long-horizon planning, (2) iterative retrieval and verification throughout sources, (3) proof monitoring with low hallucination charges, and (4) synthesis underneath massive contexts. The IterResearch rollout restructures context every “spherical,” retaining solely important artifacts to mitigate context bloat and error propagation, whereas the ReAct baseline demonstrates that the behaviors are discovered relatively than prompt-engineered. The reported scores on HLE and BrowseComp counsel improved robustness on multi-hop, tool-mediated queries the place prior brokers usually over-fit to immediate patterns or saturate at low depths.

Key options of Tongyi DeepResearch-30B-A3B

  1. MoE effectivity at scale: ~30.5B complete parameters with ~3.0–3.3B activated per token (Qwen3-MoE lineage), enabling small-model inference value with large-model capability.
  2. 128K context window: long-horizon rollouts with proof accumulation for multi-step internet analysis.
  3. Dual inference paradigms: native ReAct for intrinsic tool-use analysis and IterResearch “Heavy” (test-time scaling) for deeper multi-round synthesis.
  4. Automated agentic information engine: absolutely automated synthesis pipeline powering agentic continuous pre-training (CPT), supervised fine-tuning (SFT), and RL.
  5. On-policy RL with GRPO: Group Relative Policy Optimization with token-level coverage gradients, leave-one-out benefit estimation, and selective negative-sample filtering for stability.
  6. Reported SOTA on deep-research suites: HLE 32.9, BrowseComp 43.4 (EN) / 46.7 (ZH), xbench-DeepSearch 75; sturdy outcomes on WebWalkerQA/GAIA/FRAMES/SimpleQA.

Summary

Tongyi DeepResearch-30B-A3B packages a MoE (~30B complete, ~3B energetic) structure, 128K context, twin ReAct/IterResearch rollouts, and an automatic agentic information + GRPO RL pipeline right into a reproducible open-source stack. For groups constructing long-horizon analysis brokers, it presents a sensible stability of inference value and functionality with reported sturdy efficiency on deep-research benchmarks

ws the place precision and reliability are essential.

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