docs/ayush/task_breakdown.md

Person B (Ayush) Task Breakdown and Execution Plan

Source of truth: ReplicaLab_Comprehensive_Task_Division.md

---

1. Status

Ayush's implementation lane is complete.

Completed tasks in this lane now cover:

1. Scientist prompting and parsing
2. Baseline Scientist policy
3. Shared deterministic Lab Manager grounding contributions
4. Notebook and reusable training stack
5. ART/OpenEnv rollout-to-trainer integration
6. Metrics, plotting, evaluation, trained-policy loading, and metadata export
7. Fresh-runtime notebook smoke validation

The remaining training risk is no longer missing backlog work in Ayush's lane. It is model quality:

1. The ART/OpenEnv Scientist runtime is live and reproducible.
2. The latest live checkpoint still underperforms the deterministic baseline on held-out comparison.
3. The next useful work is experiment iteration, not infrastructure completion.

---

2. Final Verification State

The following validation steps are now complete:

1. scientist-preview smoke run
2. lab-manager-preview smoke run
3. live art-scientist-train smoke run against the hosted ReplicaLab environment
4. scientist-compare-eval smoke run against the trained checkpoint
5. focused training-policy tests and CLI tests

Smoke artifacts now exist under:

1. replicalab/outputs/training/scientist-preview-smoke-20260308/
2. replicalab/outputs/training/lab-manager-preview-smoke-20260308/
3. replicalab/outputs/art-training/art-scientist-smoke-20260308/
4. replicalab/outputs/art-training/art-scientist-compare-smoke-20260308/

---

3. Remaining External Work

No Ayush-owned backlog items remain.

Open work outside this lane that still matters to the final story:

1. TRN 12 owned by Person D: turn evaluation outputs into judge-facing result bullets
2. UI and README result presentation tasks
3. demo-storytelling tasks

These are not blockers for the training runtime itself.

---

4. Next Technical Focus

If work continues in this lane, it should target model improvement rather than missing task closure:

1. Increase Scientist training coverage beyond the current smoke scenario set
2. Inspect failure episodes from art-scientist-compare-20260308-step5 and art-scientist-compare-smoke-20260308
3. Add stronger warm-start or curriculum before more RL updates
4. Execute the Lab Manager SFT path live and evaluate its effect separately
5. Keep baseline-vs-trained comparisons on fixed seeds and frozen evidence packs
6. Track paper_understanding and communication_quality on every eval run
7. Keep the shared benchmark-history plots updating across runs
8. Use docs/training_goals.md as the near-term model-goals reference

---

5. Base Model Assumptions

Primary shared base: Qwen3.5-9B

1. Scientist uses the shared base with a GRPO-style trainable adapter.
2. Lab Manager uses the same shared base with a separate SFT adapter.
3. Qwen3.5-4B remains the lower-memory fallback.
4. Qwen3.5-122B-A10B is an audit-only judge candidate, not the reward source.
5. The deterministic rubric remains the only training reward source.

---

6. Summary Table

Category	Count	Status
Ayush-owned tasks remaining	0	Closed
Technical blockers in Ayush lane	0	Closed
Live runtime path	1	Validated
Main remaining risk	1	Model quality, not infrastructure