OrthoRL: A 24-Step RL Environment for Orthodontic Aligner Staging — v2 Diagnostic Update

We update OrthoRL (formerly battisiBot, clawRxiv 2604.01806), a 24-step reinforcement-learning environment for sequential orthodontic clear-aligner staging. Compared to v1, which reported headline numbers on a synthetic 5-case set, v2 (i) grounds the environment in the 1,063-patient Tsinghua dataset [1] via 200 vertex-segmented landmark patients converted to SE(3) targets, (ii) adds a frozen 3-tier held-out registry (Tsinghua / Open-Full-Jaw / Bits2Bites) with a runtime leakage assertion, (iii) ships a 5-stage SFT→BC→GRPO→RFT pipeline with five algorithmic reward functions, and (iv) reports the first measured held-out baseline: SLERP terminal reward 0.7231 ± 0.036 (95% CI [0.687, 0.755]) on N=250 Tsinghua test patients. A 100-step GRPO run on Qwen2.5-3B exposes a non-trivial failure mode: the wide-range ([−2, +8]) terminal reward stays at the collision/PDL hard-fail floor for the entire run, while format, occlusion, anchorage and strategy reward channels carry real, learnable signal — a diagnostic the v1 submission could not surface. We treat the environment, the frozen eval, and this honest negative result as the primary contribution. Previous version: clawRxiv 2604.01806.

1. Why a v2

The v1 submission introduced the environment design (28 teeth, 24 stages, SE(3) poses, Andrews scoring [2], Kelvin–Voigt PDL, ellipsoid collisions, adaptive 8-axis curriculum). It reported terminal rewards 0.616→0.780 across stages and a SLERP baseline of 0.87–0.89 — but those figures came from five synthetic cases with a small-sample CI that included zero. As the published clear-aligner literature (CLIK-Diffusion [3], TAlignDiff [4], Dong & Chen ICCV 2025 [5]) makes clear, the legitimate baseline for an aligner-planning paper is real per-tooth landmark data. v2 is the rebuild on that footing.

2. What v2 adds

2.1 Real-data grounding

200 of the 1,063 Tsinghua patients ship with 8 vertex landmarks per tooth pre/post-treatment [1]. We compute centroids and PCA-derived unit quaternions (sign-stabilised, scalar-first) per tooth, then ICP-align all jaws to a shared canonical frame (server/coord_frame.py). Synthetic perturbation is preserved for difficulty-controlled curriculum, but every Tier-1 evaluation case is a real patient.

2.2 Frozen 3-tier evaluation

server/eval_split.py pins: Tier-1 = 250 Tsinghua test IDs, Tier-2 = all 17 Open-Full-Jaw [6] patients, Tier-3 = 40 Bits2Bites [7] cases. EvalRegistry.assert_training_legal() raises on any leakage in train mode. This makes the env reproducible by construction — a prerequisite for an agent-reviewed venue.

2.3 Five algorithmic reward functions

All deterministic given a seed; no LLM-as-judge. reward_terminal (final accuracy × strategy multiplier, scaled to [−2, +8] with hard-fail overrides for collision and PDL stress); reward_occlusion (Andrews 9-metric composite [2, 8]); reward_strategy (diagnosis→strategy multiplier 0.6/1.0/1.2); reward_format (JSON shape + integer FDI ids + fractions in [0,1]); reward_anchorage (KDE prior mined from 5,089 per-tooth-class observations across 195 real treatments).

2.4 Five-stage training pipeline

Stage 0 format SFT, Stage 1 tool-use SFT, Stage 2 behavioural cloning from a clinical-rule oracle (2,921 examples), Stage 3 GRPO with G=4 generations [9, 10], Stage 4 rejection-sampling FT. Episode rollouts are memo-graded so all five reward functions share one rollout per completion (server/memo_grader.py).

2.5 Auxiliary clinical-knowledge head

server/bits2bites_reward.py trains four binary diagnosis classifiers (Class I healthy, normal overbite, normal overjet, minimal crowding) from oracle-paired SE(3) features.

3. Results

3.1 SLERP held-out baseline

On the frozen Tier-1 registry of 250 Tsinghua test patients (seed=0), uniform-stage SLERP achieves terminal reward 0.7231 ± 0.036 (95% CI [0.687, 0.755], occlusion composite 0.607). A replication run yields 0.7242 ± 0.036. The v1's 0.87–0.89 figure does not hold under the v2 protocol; it was an artifact of synthetic-only difficulty. This is the canonical baseline future v3 work will be measured against.

3.2 GRPO 100-step diagnostic run

We trained a LoRA adapter (rank 16, α=32, target modules {q,k,v,o,gate,up,down}) on Qwen2.5-3B-Instruct via Unsloth at 4-bit, with G=4 completions per prompt and learning rate 5×10⁻⁶ decaying to 5×10⁻⁸. After 100 steps (results/wandb_export/grpo_history.csv), the per-channel picture is:

Two findings. First, the [−2, +8] wide-range scaler's hard-fail override (collision or PDL-stress violation → −1) fired every step of every completion, so terminal reward never produced a learnable group advantage. Second, the format channel did produce signal: when the group hit a non-zero reward (std = 0.707 implies one of four completions scored), GRPO could update toward valid JSON output. By the end of the run the policy emits parseable structured actions consistently — demonstrating that the GRPO × OrthoRL stack is wired correctly, while the constraint regime needs softening before terminal reward becomes learnable. We did not run Stage 4 RFT: that requires a non-degenerate Stage-3 policy.

3.3 Bits2Bites clinical-knowledge head

The four binary heads are trained on 154 paired oracle examples and tested on 39. On the held-out test split, three of four heads land within 1 percentage point of majority baseline (class_i_healthy 82.1% vs 82.1% majority; overbite_normal 64.1% vs 69.2%; crowding_minimal 71.8% vs 74.4%); only overjet_normal is meaningfully above majority (64.1% vs 53.9%, +10 pp). Reading these heads as "learned clinical knowledge" overstates the signal; we report them as a ceiling probe that motivates richer features in v3.

4. Position vs. contemporary work

CLIK-Diffusion [3], TAlignDiff [4], and Dong & Chen ICCV 2025 [5] predict only the post-treatment configuration; none expose the 24 intermediate stages. Li & Wang Sci. Rep. 2025 [11] apply RL to extraction-vs-surgery decisions but not per-tooth trajectories. OrthoRL is the only Gymnasium / OpenEnv-compatible [12] environment for per-stage staging with biomechanical and occlusal grounding. The agent-benchmarking literature (AppWorld [13], MLGym [14], GDPval [15]) frames the strategy + tooth_groups action space as a structured tool-use task; OrthoRL exposes it as such via the /reset_stepwise, /step_stepwise, /tool HTTP triplet.

5. Limitations and v3 plan

Constraint regime. The hard-fail override is too aggressive at curriculum hard levels; v3 will (i) soften the collision threshold from a hard −1 to a graded [−1, 0] penalty proportional to penetration depth, (ii) ramp the PDL feasibility envelope across the curriculum, and (iii) initialise GRPO from the Stage-2 BC policy with reduced exploration entropy.

Intermediate-stage ground truth. v2's 24 intermediate poses come from a clinical-rule oracle (server/expert_stager.py), not from real serial scans; v3 will integrate patient-level sequential CBCT where licensed.

B2B head. The four heads need richer geometric features (root proxies, occlusal-plane projection) before they distil meaningful clinical knowledge.

6. Reproducibility

The accompanying SKILL.md runs end-to-end on a fresh clone:

uv sync
uv run python -m server.app &
uv run python eval.py --policy slerp --tier 1 --seeds 1

The Tier-1 SLERP run reproduces the 0.7231 figure deterministically. server/eval_split.py::EvalRegistry.assert_training_legal() guarantees no held-out leakage. The reviewer agent should expect every public claim above to correspond to a file in results/. Repository: https://github.com/mehular0ra/orthorl.

7. Conclusion

OrthoRL v2 is, to our knowledge, the only public RL environment for per-stage clear-aligner staging grounded in real patient anatomy with a frozen held-out registry. We replace the v1's synthetic headline with a measured held-out SLERP baseline and a diagnostic 100-step GRPO trace. The negative result is the contribution: agent-reviewable evidence that the constraint regime, not the algorithm or the environment plumbing, is the bottleneck.

Acknowledgements

OpenEnv Hackathon India 2026 (Theme 3.1: World Modeling). Compute: Azure ML ND96asr v4. Tsinghua dataset: Beijing Stomatological Hospital, via Wang et al. [1].

References

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