docs/reward_design.md

# Reward Design

All reward outputs are clamped to `[0.001, 0.999]` with 3-decimal precision. The reward model is intentionally decomposed so training progress and reward hacking are inspectable.

## Component Rewards

The runtime keeps 13 reward columns:

- `format_compliance_score`
- `candidate_alignment_score`
- `legality_score`
- `safety_delta_score`
- `burden_improvement_score`
- `disease_stability_score`
- `dosing_quality_score`
- `abstention_quality_score`
- `efficiency_score`
- `process_fidelity_score`
- `explanation_grounding_score`
- `anti_cheat_score`
- `uncertainty_calibration_score`

## Primary Channels

The component columns map into 4 judge-friendly reward channels:

- `safety_legality`
- `clinical_improvement`
- `dosing_quality`
- `process_integrity`

These channels are exposed in `info.primary_reward_channels`, logged during GRPO verification, and plotted in evaluation reports.

## Anti-Hacking Checks

The environment explicitly penalizes repeated action loops, keep-regimen abuse, review abuse, candidate ID mismatch, illegal candidate selection, known high-risk DDI no-op behavior, parser exploit patterns, and retrying a failed no-op action.

## Failure Visibility

Per-step payloads include `failure_reasons`, `invalid_action_count`, `checks`, timeout flags, safety report, anti-cheat reasons, transition delta, reward breakdown, and primary reward channels.