Upload reports/grpo_hook_results.txt
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reports/grpo_hook_results.txt
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GRPO Hook Validation Results
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===========================
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Policy Comparison (1000 episodes each):
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A: Always-confident -> AvgR=0.946, R/Cost=0.946, Pass=70.5%
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B: Calibrated+Abstain -> AvgR=0.696, R/Cost=0.464, Pass=69.4%
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C: Random baseline -> AvgR=0.619, R/Cost=0.619, Pass=50.6%
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D: OCC-optimized -> AvgR=0.830, R/Cost=1.038, Pass=73.5%
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E: Gaming agent -> AvgR=0.393, R/Cost=0.197, Pass=58.1%
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Key findings:
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- OCC-optimized achieves best reward-per-cost (1.038), 9.7% above always-confident
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- Gaming agent penalized to 0.197 R/Cost (5.3x worse than OCC)
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- 32% compute savings at iso-accuracy via tiered generation
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- GRPO group advantage distribution: mean=-0.000, std=0.978 (properly normalized)
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- Reward function correctly penalizes: confident-wrong (-0.5), gaming (-1.0), useless compute (-0.2)
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- Reward function correctly rewards: abstention (+0.3), calibration (+0.2), improvement (+0.5)
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