docs/09-risks.md

09 — Risk Register & Mitigations

Ranked by likelihood × impact. Top of list = address first.

R1 — Reward curve goes flat (HIGH likelihood, HIGH impact)

Symptom: After 100 GRPO steps, mean episode reward stays at baseline (~0.25).

Causes:

• Reward signal too sparse
• Per-step shaping too small relative to terminal reward
• Rollout parsing broken (model outputs gibberish, parser silently fails)
• KL coefficient (β) too high → policy can't move

Mitigations:

• Sanity-check rollout parser: print 5 random completions + parsed actions
• Verify shaping rewards firing: log per-step reward by action type
• Reduce β to 0.01
• Increase shaping reward magnitude (×2)
• Simplify rubric: drop InfoGain temporarily, use only FieldMatch
• Pre-warm with SFT on synthetic "ask first" trajectories (1-2 epochs)

Time to detect: 15 min (smoke test of 100 steps) Time to fix: 30-60 min

R2 — Reward hacking (HIGH likelihood, MEDIUM impact)

Symptom: Reward curve climbs but qualitative outputs are gibberish/repetitive.

Likely hacks:

• Always ask same generic question 6 times then submit empty plan
• Submit JSON with all profile field keys but garbage values
• Output the same action token over and over

Mitigations:

• Duplicate-Q penalty (already in plan)
• HallucinationCheckRubric (already in plan)
• FormatCheck Gate with strict schema (already in plan)
• Add EntropyRubric: penalize repeated actions (component if needed)
• Manual inspection of 10 trained outputs every 100 steps

Time to detect: 100 GRPO steps + manual inspection (15 min) Time to fix: 30 min (add penalty component)

R3 — Colab session times out mid-training (MEDIUM, MEDIUM)

Symptom: Long training run gets killed by Colab free-tier session limits.

Mitigations:

• Save LoRA checkpoint every 100 steps
• Always run training in resumable form (TRL supports resume from checkpoint)
• Plan training in 100-step chunks, not one mega-run
• Have second Google account ready for backup

Time to detect: live Time to fix: 5 min (resume from last checkpoint)

R4 — HF Space build fails (MEDIUM, HIGH)

Symptom: git push space main succeeds but Space build errors out.

Common causes:

• Dockerfile issues (missing deps, wrong Python version)
• pyproject.toml resolution failure
• HF Space hardware mismatch

Mitigations:

• Test Docker build LOCALLY before pushing: docker build -t clarify-rl . && docker run -p 8000:8000 clarify-rl
• Mirror EXACT Dockerfile from working SRE env (which we know builds)
• Push minimal stub Space FIRST (just FastAPI hello world), confirm builds, then layer on env
• Keep Space build logs open in browser tab while pushing

Time to detect: 5-10 min (HF build logs) Time to fix: 15-30 min (Docker iteration)

R5 — Validator rejects submission (LOW likelihood, FATAL impact)

Symptom: Auto-validator marks submission incomplete; never reaches human judges.

Mitigations:

• Run through every item in docs/07-deployment.md checklist
• 1-hour pre-deadline buffer for fixes
• Test ALL deliverable links from incognito browser
• Make sure plots are committed as files, not just in notebook outputs

Time to detect: post-submission (TOO LATE — must validate before) Time to fix: depends on what's missing

R6 — Training takes too long on T4 (LOW, MEDIUM)

Symptom: 600 GRPO steps take >2 hours; eats into Day 2 schedule.

Mitigations:

• Use Unsloth (we already are)
• Use 4-bit quantization (we already are)
• Reduce max_seq_length to 2048 if needed
• Reduce num_generations to 2 (instead of 4)
• Stop at 300 steps if curve is good — quality > quantity

Time to detect: 30 min into training (extrapolate) Time to fix: tune config, restart from checkpoint

R7 — Rubric doesn't separate good from bad (LOW, HIGH) — ✅ VERIFIED OK

Symptom: Even oracle policy gets ~0.5; even random policy gets ~0.5.

Causes:

• Weights wrong, components average out
• FormatCheck too lenient
• HallucinationCheck too punitive

Mitigations:

• Run sanity policies BEFORE training:
  • Random: should get ~0.20
  • Oracle (asks all critical Qs, perfect plan): should get ~0.95
  • Blank plan: should get 0.0
• If gap is small, retune weights and component logic before training

Current status: Oracle scores ~0.89 via smoke_env.py (FormatCheck=1.0, FieldMatch=1.0, InfoGain=1.0, Efficiency=0.5, Hallucination=0.75). Gap is healthy.

Time to detect: 10 min (sanity script) Time to fix: 30-60 min

R8 — Profile generator produces unsolvable scenarios (LOW, MEDIUM) — ✅ MITIGATED

Symptom: Even oracle can't get high score on some scenarios.

Causes:

• Field vocabulary too sparse → user simulator returns wrong field
• Critical fields not always present
• Request template too vague to even hint at task type

Mitigations:

• Validate generator: 100 random scenarios → oracle scores them → all should be ≥0.7
• Add task_type hint to every request template (subtle, e.g. "dinner" → restaurant)
• Ensure FIELD_KEYWORDS covers all profile fields

Fix applied: scenarios.py now always includes required_keys in the profile for medium/hard difficulty. Hard range adjusted to (6,7) to match actual field pool sizes (max 7).

Time to detect: 5 min (sanity check) Time to fix: 15-30 min

R9 — One team member becomes unavailable (LOW, HIGH)

Symptom: Anurag or Kanan can't continue (illness, technical issues, lost device).

Mitigations:

• Both can git-push to both remotes
• Both have HF + GitHub credentials
• Both have Colab access
• Pair-program critical sections (env, rubric)

Time to detect: live Time to fix: depends, but project should continue

R10 — Last-minute organizational changes (LOW, VARIABLE)

Symptom: Submission form changes, deadline shifts, theme reinterpretations announced.

Mitigations:

• Monitor Discord every 2 hours
• Both team members on Discord notifications
• Have a Plan B for each deliverable (video OR blog, not both required)

Fallback Plans (graceful degradation)

If we run out of time:

1. Cut difficulty levels: Ship only "medium" task — still scores well on Storytelling
2. Cut task types: Ship 3 of 5 task types instead of all 5
3. Cut training: Use Unsloth pre-trained on synthetic SFT data, skip GRPO. Worse story but still ships.
4. Cut video: Ship blog post only.
5. Cut blog: Ship video only.

The core ship is: HF Space + Colab + plots + README. Everything else is bonus.

Risk Score Summary

ID	Risk	L	I	Score
R1	Reward curve flat	H	H	9
R2	Reward hacking	H	M	6
R3	Colab timeout	M	M	4
R4	HF Space build fail	M	H	6
R5	Validator rejection	L	F	5
R6	Training too slow	L	M	2
R7	Rubric doesn't separate	L	H	3
R8	Bad scenarios	L	M	2
R9	Team member down	L	H	3
R10	Org changes	L	V	1

L=likelihood, I=impact, F=fatal.

Top 3 to actively mitigate during build: R1, R2, R4.