feat: implement dataset loader, environment, and GRPO training pipeline for undertrial bail prediction

modified

implemented

Fix A3 (OOM eval), B9 (NDPS eligibility), B3 (direction-gated computation bonus), A8-pt2 (episode_id case lookup)

Fix 8 compliance gaps: repeat-action dedup+cache, min-steps hard block, criminal history tool (12th action), efficiency removed from training formula, circular import cleaned, yaml formula synced

Reward overhaul: add compute_reasoning_quality (anchoring+arithmetic+specificity+consistency), parity-grounds penalty, reduce outcome 40%->30%, add 10% reasoning quality signal

Fix ACTION_MAP gaps: add 4 new tools to REST + WebSocket handlers; remove StepResult import collision

Add 4 missing actions: read_submissions, assess_flight_risk, check_case_factors, apply_proportionality (fixes 4.3d/e/g/h/i)

feat: implement core UndertriAI OpenEnv training environment with tool dispatch and reward logic

Fix all audit gaps: custody neutral, parity-first bias, skip penalty 0.40, statutory process reward, /observation endpoint, reset() timeout, drift determinism

Fix 5 audit gaps: conditional bail, action history, efficiency reward, train/val split, env API routing

Fix 6 vulnerabilities: /state crash, reward clamp, condition reward, XML exploit, tool-skip bypass, timeout enforcement

Add seed param to /reset: demo pins to seed=0 per stage for consistent known episodes

Fix crash: call super().__init__() so self.rubric is set before _reset_rubric()

OpenEnv compliance: proper base class, SUPPORTS_CONCURRENT_SESSIONS, state @property, updated openenv.yaml

first commit