StepFun has launched Step-DeepResearch, a 32B parameter finish to finish deep analysis agent that goals to show net search into precise analysis workflows with lengthy horizon reasoning, device use and structured reporting. The mannequin is constructed on Qwen2.5 32B-Base and is skilled to behave as a single agent that plans, explores sources, verifies proof and writes studies with citations, whereas preserving inference value low.
From Search to Deep Analysis
Most current net brokers are tuned for multi-hop question-answering benchmarks. They attempt to match floor fact solutions for brief questions. That is nearer to focused retrieval than to actual analysis. Deep analysis duties are totally different. They contain latent intent recognition, lengthy horizon resolution making, multi-turn device use, structured-reasoning and cross-source verification beneath uncertainty.
Step-DeepResearch reframes this as sequential resolution making over a compact set of atomic capabilities. The analysis staff defines 4 atomic capabilities, planning and job decomposition, deep-information looking for, reflection and verification, {and professional} report technology. As an alternative of orchestrating many exterior brokers, the system internalizes this loop right into a single mannequin that decides the subsequent motion at every step.
Knowledge Synthesis round Atomic Capabilities
To show these atomic capabilities, the analysis staff builds separate information pipelines for every talent. For planning, they begin from prime quality technical studies, survey papers and monetary evaluation paperwork. They reverse-engineer lifelike analysis plans and job timber from titles, abstracts and construction, then generate trajectories that comply with these plans. This exposes the mannequin to lengthy horizon mission buildings, not solely quick query templates.
For deep info looking for, they assemble graph primarily based queries over data graphs reminiscent of Wikidata5m and CN-DBpedia. They pattern subgraphs, develop them utilizing search, and synthesize questions that require multi hop reasoning throughout entities and paperwork. A separate pipeline makes use of a Wiki model hyperlink index to power cross doc retrieval and mixture of proof. Simple questions {that a} robust mannequin can already clear up with a easy ReAct model technique are filtered out, so coaching focuses on arduous search issues.
Reflection and verification information is generated by means of self-correction loops and multi-agent instructor traces. Instructor brokers extract claims, plan checks, confirm info, replan if inconsistencies seem and solely then write studies. The ensuing trajectories are cleaned and used as supervision for a single scholar agent. Report technology is skilled in 2 phases, mid coaching for area model and depth utilizing question report pairs, then supervised fine-tuning with strict formatting and plan consistency constraints.
Progressive Coaching on Qwen2.5-32B-Base
The coaching pipeline has 3 levels, agentic mid-training, supervised fine-tuning and reinforcement studying. In mid coaching stage-1, the staff injects atomic capabilities with out instruments, utilizing context size as much as 32k tokens. The information covers energetic studying, artificial reasoning traces, summarization and reflection. The analysis staff present regular good points on SimpleQA, TriviaQA and FRAMES as coaching scales as much as about 150B tokens, with the biggest good points on FRAMES, which stresses structured reasoning.
In stage-2, the context extends to 128k tokens and express device calls are launched. The mannequin learns duties reminiscent of URL primarily based question-answering, deep net search, lengthy doc summarization and lengthy dialogue reasoning. This stage aligns the mannequin with actual analysis situations the place search, looking and evaluation should be blended in a single trajectory.
Throughout supervised fine-tuning, the 4 atomic capabilities are composed into full deep search and deep analysis traces. Knowledge cleansing retains trajectories which can be appropriate and quick by way of steps and gear calls. The pipeline injects managed device errors adopted by correction to enhance robustness, and enforces quotation codecs in order that studies keep grounded within the retrieved sources.
Reinforcement studying then optimizes the agent in an actual device atmosphere. The analysis staff builds duties and checklists by means of reverse synthesis, and trains a guidelines model Rubrics Decide to attain studies alongside nice grained dimensions. The reward design converts ternary rubric labels into uneven binary rewards that seize each optimistic targets and violations. The coverage is skilled with PPO and a realized critic, utilizing generalized benefit estimation with close to zero low cost in order that lengthy trajectories will not be truncated.
Single Agent ReAct Structure and Search Stack
At inference time, Step-DeepResearch runs as a single ReAct model agent that alternates pondering, device calls and observations till it decides to output a report. The device set consists of batch net search, a todo supervisor, shell instructions and file operations. Execution runs in a sandbox with terminal persistence by means of tmux. A notion oriented browser reduces redundant web page captures by utilizing perceptual hash distance. Instruments for doc parsing, audio transcription and picture evaluation assist multimodal inputs.
Info acquisition makes use of 2 associated assets. StepFun staff states that its Search API is grounded in additional than 20M prime quality papers and 600 premium indices. The analysis staff then describes a curated authority indexing technique that isolates greater than 600 trusted domains, together with authorities, educational and institutional websites. Retrieval operates at paragraph stage and makes use of authority conscious rating so that prime belief domains are most popular when relevance is comparable.
The file instruments assist patch primarily based modifying, so the agent can replace solely modified sections of a report. A abstract conscious storage scheme writes full device outputs to native recordsdata and injects solely compact summaries into the context. This acts as exterior reminiscence and avoids context overflow for lengthy initiatives.
Analysis, Price and Entry
To measure deep analysis habits, the staff introduce ADR-Bench, a Chinese language benchmark with 110 open ended duties throughout 9 domains. 70 duties cowl common domains reminiscent of training, science and engineering and social life, evaluated by professional aspect by aspect comparability. 40 duties in finance and legislation are scored with express rubrics that comply with atomicity and verifiability constraints.
On Scale AI Analysis Rubrics, Step-DeepResearch reaches 61.42 % rubric compliance, which is akin to OpenAI-DeepResearch and Gemini-DeepResearch, and clearly forward of a number of open and proprietary baselines. On ADR-Bench, expert-based Elo scores present that the 32B mannequin outperforms bigger open-models reminiscent of MiniMax-M2, GLM-4.6 and DeepSeek-V3.2, and is aggressive with programs like Kimi-Researcher and MiniMax-Agent-Professional.
Key Takeaways
- Single agent, atomic functionality design: Step-DeepResearch is a 32B parameter single agent constructed on Qwen2.-32B-Base, it internalizes 4 atomic capabilities, planning, deep info looking for, reflection and verification, {and professional} report technology, as an alternative of counting on many exterior brokers.
- Focused information synthesis for every talent: The analysis staff builds separate information pipelines for planning, deep info looking for, reflection and report writing, utilizing reverse-engineered plans from actual studies, graph-based queries over Wikidata5m and CN-DBpedia, multi-agent instructor traces and strict report formatting information.
- Three stage coaching with lengthy context and RL: Coaching makes use of mid coaching, supervised fine-tuning and reinforcement studying, with mid coaching as much as 150B tokens at 32k after which 128k context, SFT composes full deep analysis trajectories, and PPO primarily based RL with a Rubrics Decide optimizes studies towards nice grained checklists.
- ReAct structure with curated search and exterior reminiscence: At inference time the mannequin runs a ReAct loop that calls instruments for batch net search, todo, shell and file operations, makes use of a Search API grounded in additional than 20M papers and 600 premium indices together with 600+trusted domains, and depends on patch modifying and abstract conscious storage to behave as exterior reminiscence.
- Aggressive high quality with decrease value: On Scale AI Analysis Rubrics the mannequin reaches 61.42 % rubric compliance and is aggressive with OpenAI-DeepResearch and Gemini-DeepResearch, on ADR Bench it achieves 67.1 % win or tie fee towards robust baselines.
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