| [x] **Task 1: Workload Realism & BurstGPT Validation** |
| - [x] Process raw BurstGPT into Parquet pools |
| - [x] Implement Chiron (2024) Gaussian noise jitter |
| - [x] Implement Sarathi-Serve "Mega-Prompt" stall logic |
| - [x] Verify statistical matching and spike detections. |
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| 2. Reward Function & RL Shaping |
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| Credit Assignment: Verify that every sub-component of the reward (throughput, SLO compliance, memory, cost) updates accurately at every step based only on the most recent action. |
| Goldilocks Dynamics: Test if the memory pressure penalty actually encourages the agent to keep KV cache occupancy in the optimal 60–85% target zone. |
| Exploit Hunting: Intentionally try to cheat the reward function (e.g., dropping all traffic to save memory, or setting infinite batch sizes) to ensure penalties protect the primary SLO constraints. |
| 3. Simulator vs. Reality Calibration |
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| Latency Lookup Tables: Compare the heuristic fallback numbers in simulated.py (e.g., p99_ttft, p50_itl) against real benchmarks like the vLLM and Orca papers. |
| Memory Economics: Ensure the math linking batch_cap, kv_budget_fraction, and gpu_memory_used_gb intuitively reflects real PagedAttention allocator fragmentation. |
| 4. Task Definition & Difficulty Validation |
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| Difficulty Curves: Run the random, heuristic, and PPO agents to experimentally confirm that the score spread clearly differentiates the easy, medium, and hard tasks. |
| Task 3 Hardness: Guarantee that the adversarial_multitenant task is genuinely unsolvable by static rules and forces the agent to learn dynamic priority routing. |
| 5. System Robustness & Evaluation Compliance |
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| Determinism: Heavily test that seeding env.reset(seed=X) guarantees 100% bit-identical observations across thousands of steps. |
| OpenAPI Inference Limits: Time the full |
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| inference.py |
| loop across all three tasks using an LLM to guarantee it never breaches the strict 20-minute hackathon constraint. |
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