Delete baseline/__pycache__

Fix Env: Change Model

Expose data_lag_days in CityObservation; remove fragile num_districts==6 inference

Reduce rollouts easy=2 medium=3 hard=3 — target ~15min runtime with buffer for judge overhead

Fix 8 issues spotted in code review: Dockerfile path, task descriptions, duplicate function, fallback score consistency, twin constants comment, has_data_lag docs

Remove Validation.py

Cut easy rollouts 3→2 to stay under 20min; update GRPO scores; fill config/tasks.yaml

Fix All: Cleaned All Files

Fix Readme: Corrected Grader Percentages

Easy rollouts 2→3 for better score stability, still under 20min

Per-task rollouts: easy=2, medium=4, hard=4 — reduces runtime to ~16min

Reduce max_tokens 120→60 — agent uses ~32 tokens, no content affected

Fix Inference,Evaluator: Increased Rollouts to 4

Fix Inference,Env: Change Model

Fix memory threshold, pre-compute lag estimates in hard prompt

Fix memory threshold, pre-compute lag estimates in hard prompt

Fix Inference,Env: Change Model

Fix Evaluator: Decrease Rollouts to 3

Fix evaluator:Increase number of rollouts to 8 for better performance.

Improve agent: fix strategy rule, chain-of-thought, 8 rollouts, better memory

Fix treatment/recovery calibration, hospital breach reward, efficiency grader, recalibrate seeds

Fix realism: natural recovery in spread, hospital breach at 10%, linear spillover, correct prompt

Increase rollouts to 5 for better GRPO learning convergence

Swap grader weights: hospital=45% primary constraint, containment=30%, calibrate environment

Lower spread rate to 0.09, fix medium seeds, teach focused allocation strategy

Raise treatment reduction, lower medium/hard seeds, focus prompt strategy

Fix resource replenishment, grader efficiency, seed calibration, prompt strategy

Add /grade endpoint, use real grader scores in evaluator

Add README.md

Initial submission: Cascade Containment