> ## Documentation Index > Fetch the complete documentation index at: https://docs.arc.computer/llms.txt > Use this file to discover all available pages before exploring further. # Adaptive Dual-Agent Reasoning > How your agent partners with a verifying teacher to deliver safer, higher-quality results. ATLAS hinges on an adaptive dual-agent reasoning loop: your production agent (the **student**) stays frozen, while a specialized **verifying teacher** evaluates its plan, provides targeted guidance, and confirms the final answer. The partnership boosts quality without touching model weights and works across any provider—API-only or self-hosted. Think of it as pairing every agent run with an expert reviewer. The reviewer doesn’t replace your agent; it inspects the approach, corrects mistakes, and signs off before results ship to users. ## Core Concept * **Student (your agent)** – Any LLM or tool stack executing the task (GPT, Claude, Gemini, local checkpoints, custom code). * **Verifying teacher** – An 8B specialized model trained to diagnose gaps, inject guidance, and certify answers. * **Outcome** – Better answers, higher safety, and richer telemetry—without retraining the underlying agent. ### Why it’s model agnostic | Traditional approach | Adaptive dual-agent reasoning | | ------------------------------ | ------------------------------------------------- | | Retrain or fine-tune the agent | Keep the agent frozen and add a verifying teacher | | Requires model access & GPUs | Works with API-only models | | Risk of regression | Preserves baseline capabilities | | Weeks to deploy updates | Hours to roll out new teacher checkpoints | For runtime implementation details, see [`How Orchestration Works`](/sdk/orchestration) for the runtime [`personas`](/reference/glossary#persona) and [`Offline Training`](/training/offline/grpo-training) to learn how new teachers are trained. ## Why it works ### 1. Asymmetric specialization The teacher focuses solely on teaching. It doesn’t need to outperform the agent at solving tasks; it needs to spot blind spots, orchestrate retries, and provide precise interventions. > **Analogy:** A senior reviewer doesn’t code faster than the whole team—they prevent critical mistakes, guide architecture decisions, and approve releases. ### 2. Inference-time enhancement Guidance happens through prompts at runtime: 1. The teacher inspects the task, telemetry, and prior attempts. 2. It scores capability, triages risk, and drafts guidance. 3. The guidance is merged into the agent’s context. 4. The agent re-runs with the added teaching and produces the final answer. No gradient steps, checkpoints, or weight updates are required. ### 3. Adaptive intensity The teacher adjusts effort based on confidence: * **High confidence:** Light-touch verification or a single checklist. * **Medium confidence:** Paired review of the final result. * **Low confidence:** Step-by-step coaching with retries. You pay only for the oversight you need, run-by-run. ## Deployment requirements | Component | Specification | Purpose | | ----------------- | ------------------- | -------------------------------------------- | | Verifying teacher | 8B RL-trained model | Generates adaptive guidance & certifications | | Student agent | Any size / provider | Executes the actual work | | Context window | 4k–32k tokens | Accommodates guidance + agent output | | Latency overhead | \~30% | Extra pass for analysis and teaching | ## Performance snapshot (τ²-bench, mms\_issue subset) | System | Pass\@1 | Notes | | ----------------------- | --------- | ---------------------------------- | | **ATLAS dual-agent** | **24.0%** | Minimal degradation across retries | | GPT-4.1 | 18.0% | −8 pts from Pass\@1 to Pass\@4 | | Claude 3.7 Sonnet | 18.0% | −16 pts drop across retries | | o4-mini | 12.0% | −10 pts drop across retries | | Qwen3-8B (student only) | 4.1% | No teacher guidance | **Key takeaways** * **6× lift** on the same student by adding the verifying teacher. * **Stable retries:** The teacher keeps success rates high on subsequent attempts. * **Cross-domain transfer:** A math-trained teacher can supervise telecom debugging tasks because it enforces process, not domain answers. ### Aggregate impact * **Average accuracy gain (runtime + GRPO):** +15.7 % * **Maximum domain lift:** +29.6 % * **Non-degradation rate:** 97 % * **Token efficiency:** \~50 % reduction * **Completion rate:** +31 % ## Benefits vs. other approaches ### Fine-tuning / RLHF * No retraining or weight access required. * Zero risk of catastrophic forgetting. * Deploy new guidance in hours, not weeks. ### Prompt engineering * Adaptive, not one-shot tuning. * Systematic and measurable improvements. * Token use scales with confidence (fast lanes stay cheap). ### Ensembles * Single agent executes the work; no multi-model voting. * Lower cost and latency for comparable quality. * Guidance is inspectable and auditable. ## Training the verifying teacher 1. **Supervised warmup (SFT)** – Teach baseline review behaviors (4–6 hours on 8× H100). 2. **GRPO fine-tuning** – Optimize for student improvement and calibrated confidence (24–36 hours on 8× H100). Rewards come from measured student gains, so the teacher is incentivized to deliver guidance that genuinely improves outcomes. ## Integration patterns ### SDK runtime orchestration Integrate via the SDK to wrap your agent with adaptive lanes (auto, paired, coach). The runtime logs every teaching decision and reward signal for later analysis. ### Offline GRPO training Export runtime traces (`arc-atlas --output traces.jsonl`) and run `atlas-core offline-pipeline --export-path .jsonl` to produce updated teacher checkpoints. Point the SDK back at the new weights to close the loop. ## Best practices * **ATLAS-8B-Thinking**: Analytical, math-heavy domains * **ATLAS-8B-Instruct**: Code generation, structured workflows * **Custom GRPO**: Train on your exported traces for domain-specific oversight Confirm the agent supports: * System prompts or instruction conditioning * Sufficient context window (>4k tokens) * Deterministic decoding (control temperature/top\_p) * Cache teacher guidance for repetitive tickets * Batch similar tasks for throughput * Stream intermediate verdicts for human-on-the-loop monitoring * Track token budgets per lane to manage cost ## Next steps Dive into the lane logic, telemetry, and orchestration flow. Learn how to operate the dual-agent loop in production infrastructure. Understand how guidance quality is quantified. Explore τ²-bench results in detail. Run SFT + GRPO to evolve your verifying teacher. ## References * [ATLAS Technical Report](/reference/technical-report) — Architecture and evaluations * [Offline Training Guide](/training/offline/grpo-training) — Hands-on teacher training walkthrough * [Adaptive Tool Use](/examples/adaptive-tool-use) — Production example with MCP integration