AI Agent Architectures
CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learning
CoDA is a novel hierarchical agent framework addressing 'Context Explosion' in Large Language Model (LLM) agents. It decouples high-level planning from low-level execution using a single shared LLM operating in distinct Planner and Executor roles. Trained with PECO, a reinforcement learning methodology, CoDA achieves state-of-the-art performance on multi-hop question answering benchmarks and demonstrates superior robustness in long-context scenarios by maintaining stable performance where other baselines degrade.
Executive Impact & Key Takeaways
CoDA's innovative architecture and training methodology deliver significant advancements in LLM agent capabilities, particularly for complex, information-intensive tasks.
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Accuracy Boost on Multi-Hop QA
Stable
Robustness in Long Contexts
Yes
Context Decoupling
PECO
End-to-End RL Training
Deep Analysis & Enterprise Applications
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AI Agent Architectures
Reinforcement Learning
Hierarchical Design for Context Management
CoDA's architecture explicitly tackles the context explosion problem inherent in monolithic LLM agents by decoupling high-level planning from low-level execution.
| Feature | Monolithic Agents | CoDA (Hierarchical) |
|---|---|---|
| Context Handling | Accumulates all history in a single context, leading to 'Context Explosion' and reasoning failures in long contexts. | Decouples context into Planner (Strategic Context Cp) and Executor (Temporary Execution Context CE). Planner remains clean from raw search results. |
| Roles | Single agent performs both planning and execution within the same context, leading to 'Context Entanglement'. | Single shared LLM acts as high-level Planner (decomposes tasks) and low-level Executor (tool interactions, information synthesis) in isolated contexts. |
| Computational Complexity (Space) | O(H · Ldoc) where H is planning hops, Ldoc is raw document length. Quadratic growth in time O((H·Ldoc)²). | O(max(H·Lres, h·Ldoc)) where Lres is concise result length, h is executor hops (small). Avoids quadratic growth due to Lres << Ldoc. |
| Robustness to Long Context | Performance degrades sharply (e.g., AutoRefine F1 drops 52%). | Maintains stable and high performance (Figure 4a). |
PECO: Joint Optimization Workflow
Planner Action (Generate Task/Answer)
→
Executor Sub-Loop (Handle Task, Use Tools, Refine Info)
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Return to Planner (Result as part of Strategic Context)
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Trajectory Collection (for RL Training)
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Group-Level Credit Assignment
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Context-Dependent Policy Update
PECO (Planner-Executor Co-Optimization) enables end-to-end training. A single trajectory-level reward updates the shared policy model, with gradients calculated based on role-specific contexts. PECO training boosts F1-score by 8 percentage points over prompt-guided baseline.
CoDA's Long-Context Robustness in Action
Scenario: Multi-hop QA requiring identification of production company, its HQ city, and bus station in that city.
Legacy Approach (AutoRefine): Issues single broad query, returns noisy/ambiguous results. Early hallucination (Sony Pictures Animation), wrong reasoning path, incorrect final answer (Culver City).
CoDA Approach: Planner decomposes problem into three distinct sub-tasks. Executor handles each in isolated context, formulates precise search queries, gets correct entity 'Nelvana', 'Toronto, Ontario', 'Toronto Coach Terminal'. Prevents information dilution and preserves reasoning quality. As the paper states: "The accumulation of unfiltered, verbose information in a single context window pollutes the reasoning process, making the monolithic agent prone to early-stage errors, thus leading to final failure. However, CoDA's design principle of context decoupling directly leads to reliable task-decomposition and step-by-step reasoning."
Visual reference: Table 5 from the paper illustrates this case study.
+8pct pts
F1-score improvement from PECO over prompt-guided baseline
Ablation study shows PECO-trained model achieves an F1-score of 0.50, a substantial 8 percentage point improvement over the prompt-guided baseline's score of 0.42. This gap is particularly evident on complex multi-hop QA datasets.
Composite Reward Function Breakdown
CoDA employs a multi-faceted reward function for accuracy, procedural correctness, and semantic understanding.
| Reward Component | Description | Average F1-score (CoDA-Base) |
|---|---|---|
| Answer Correctness (Rans) | Based on F1 score between final answer and ground-truth. Scaled to [-3, 3]. | 0.190 |
| Format Compliance (Rformat) | Rewards correct XML-style tag generation for Planner and Executor outputs. | 0.500 (with Rans & Rformat) |
| Refinement Quality (Rrefine) | Incentivizes distilling critical information from search results, ensuring non-empty summary with ground-truth. | 0.509 (with Rans, Rformat & Rrefine) |
| Total Reward (CoDA) | Weighted sum of Rans, Rformat, Rrefine. Provides dense signals for accuracy, procedural correctness, and semantic understanding. | 0.509 |
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