Unlocking Smarter AI Reasoning
SD-Search: Self-Distilled AI for Advanced Search-Augmented Reasoning
Explore how SD-Search redefines AI's ability to learn from its own search actions, achieving superior reasoning performance without external teachers or costly annotations.
Key Innovations & Business Impact
SD-Search introduces a novel self-distillation approach that significantly enhances search-augmented reasoning, offering tangible benefits for enterprise AI deployment.
0.0
Average Exact Match (7B)
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Points vs. Thinker (7B)
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Training Overhead (In-loop)
No External Teacher
SD-Search achieves state-of-the-art results without relying on larger teacher models or expensive GPT-4o annotations, reducing TCO.
SD-Search Training Process
Policy Rollout (Student)
→
Hindsight Conditioning (Teacher)
→
JSD Loss Calculation
→
Policy Update (GRPO)
| Feature | Traditional RL | SD-Search |
|---|---|---|
| Step-Level Supervision | No (Trajectory-level only) |
|
| External Teacher | No |
|
| Cost of Supervision | Low (Outcome-reward) |
|
| Query Quality Improvement | Indirect |
|
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Case Study: Multi-Hop QA (2Wiki)
In a 2Wiki multi-hop question, AutoRefine fails with a collapsed query. Thinker provides a coarse answer with generic queries. SD-Search issues two focused queries, achieving the correct date by surgically aiming at facts needed. This demonstrates SD-Search's superior query quality over search volume.
Outcome: SD-Search achieves 21 January 1426, while AutoRefine gets 1400 and Thinker gets 1426 (coarse).
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Avg. EM (3B)
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HotpotQA gain (3B)
Calculate Your AI ROI Potential
Estimate the annual savings and reclaimed hours your enterprise could achieve with advanced AI reasoning agents.
Your Enterprise AI Implementation Roadmap
Our phased approach ensures a smooth and effective integration of SD-Search capabilities into your existing workflows.
Phase 1: Discovery & Strategy
Understand current reasoning gaps and define success metrics.
Phase 2: Pilot & Integration
Deploy SD-Search in a controlled environment and validate performance.
Phase 3: Scaling & Optimization
Expand across departments and continuously fine-tune for maximum impact.
Ready to Transform Your Enterprise AI?
Connect with our experts to explore how SD-Search can elevate your organization's reasoning capabilities.
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