Agents-A1: A 35B MoE Agentic Model Reaching Trillion-Parameter-Level Performance via Agent-Horizon Scaling

Forum topic · 小凯 · 2026-07-01

Summary

Agents-A1 is a 35B-parameter Mixture-of-Experts agentic model that reportedly matches trillion-parameter models on long-horizon agent benchmarks by scaling the agent horizon instead of parameter count. The authors explore horizon scaling along two axes: longer agentic trajectories and heterogeneous agent abilities. They built a long-horizon knowledge-action infrastructure linking external knowledge, actions, observations, and verifier outcomes to generate agentic trajectories averaging 45K tokens. Training follows a three-stage recipe: full-domain supervised fine-tuning to align the base model with broad agentic behaviors; domain-level teacher models capturing specialized expertise; and multi-teacher domain-route on-policy distillation with vocabulary alignment, unifying six heterogeneous domains into one deployable student model. Reported results include SEAL-0 (56.4), IFBench (80.6), HiPhO (46.4), FrontierScience-Olympiad (79.0), and MolBench-Bind (56.8), leading models such as Kimi-K2.6 and DeepSeek-V4-pro, while remaining competitive on SciCode (44.3), HLE (47.6), and BrowseComp (75.5). Paper: arXiv 2507.00010.

Paper Overview

Research Area: Agent Authors: Lei Bai, Zongsheng Cao, Yang Chen Published: 2026-07-01 arXiv: 2507.00010

Abstract (English)

We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. To support this goal, we build a long-horizon knowledge-action infrastructure that connects external knowledge, actions, observations, and verifier outcomes, producing agentic trajectories with an average length of 45K tokens. Based on this, we train Agents-A1 with a three-stage recipe. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose a multi-teacher domain-route on-policy distillation, together with significant vocabulary alignment, to improve cross-domain knowledge transfer efficiency, unifying six heterogeneous domains into one deployable student model.

Benchmark Results

Compared with trillion-parameter models such as Kimi-K2.6 and DeepSeek-V4-pro, Agents-A1 achieves leading results on:

SEAL-0: 56.4
IFBench: 80.6
HiPhO: 46.4
FrontierScience-Olympiad: 79.0
MolBench-Bind: 56.8

And remains highly competitive on:

SciCode: 44.3
HLE: 47.6
BrowseComp: 75.5

Takeaway

The authors hope this work provides the community with a practical path to reach or match trillion-parameter model performance on long-horizon tasks using a 35B agentic model by scaling the agent horizon rather than parameters.

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