docs/SESSION_LOG.md

SESSION LOG — append-only history

Newest entries on TOP. Each agent appends one entry at session end. Keep entries to 5-7 bullets max.

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2026-04-26 07:25 IST — Claude (Cursor) — Phase 11: submission lap CLOSED + final polish

• Run 4 weights mirror to anurag203/clarify-rl-run4-qwen3-1.7b-beta0.2 is LIVE (started 04:17, completed 06:33, 2h16min total for snapshot_download + upload_folder of the 6.88 GB model). model.safetensors resolves to a 6,882,335,328-byte presigned cas-bridge.xethub.hf.co URL with X-Xet-Cas-Uid=public — judges can download without auth. All 3 mirrors (Run 1, Run 2, Run 4) confirmed private=False via HF API.
• Auto-watcher script (/tmp/clarify-rl-mirror/await_mirror_and_finalize.py) self-piloted cleanup at 06:33:03 — stripped the fallback block from docs/model_cards/run4-qwen3-1.7b-beta0.2.md, committed edd1efe, pushed to GitHub main and HF Hub model repo. Hero model is now self-contained on the personalized mirror.
• README restructured for judges (commit 9240521 and earlier): added ## ⏱ Judges — 60-second tour at top with 5-step flow + Problem/Environment/Results/Why-it-matters arc + 1-line caption under every plot + Wild Card #5 promoted into the title block + embedded assets/demo_replay_screenshot.png (Replay viewer tab showing a Run 4 rollout with per-rubric breakdown, 1440x1100 / 187 KB, captured via headless Chrome).
• Run 4 model card YAML cleanup (commit 2e31357): removed the datasets: agarwalanu3103/clarify-rl pill that HF Hub was rendering as a disabled grey tag (it was the env Space, not a dataset). Pushed cleaned card to GitHub + HF Hub. Confirmed cardData.datasets: ABSENT and tags with dataset prefix: [] on the API.
• Final logged-out smoke test: 15/15 README-referenced URLs return HTTP 200 (env Space, demo Space, all 3 model mirrors, Colab notebook, GitHub README, raw plots, blog, model cards). Env Space POST /reset returns a real CallToolObservation (meeting_scheduling family, 6-question budget, proper instructions). Submission auto-validator gates all GREEN.
• Files touched this phase: README.md (60s tour + screenshot embed + Wild Card promotion), docs/STATUS.md (mirror progress + final state), docs/model_cards/run4-qwen3-1.7b-beta0.2.md (fallback note removed by watcher + datasets pill removed), docs/slides.md (path-typo fix server/rubric/ → server/rubrics.py), assets/demo_replay_screenshot.png (new).
• Final spend: ≈ $5.8 of $120 HF Jobs budget across 3 trained runs + 1 base eval + 1 Run 4 eval. ~9.5 h to the 5 PM IST deadline at log close. All deliverable links live and public.

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2026-04-25 23:55 IST — Cascade (Cursor) — Phase 9: TWO root-cause bugs found and fixed

• Run 2 launched (Qwen3-1.7B / 400 steps / a100-large) — currently downloading deps + bootstrapping vLLM. Runs to completion in ~3h.
• Root cause #1 — parser couldn't parse what the trained model emits. Trained 0.6B emits function_call(arg="value") form (e.g. ask_question(question="What is your budget? (in USD)")) — the OLD parser used a naive regex that broke on nested parentheses in the question text. Rewrote parse_tool_call with:
  • _find_balanced_func_call(text): walks the string with a paren+quote depth counter to extract (name, body) even when body contains (...) and JSON.
  • _parse_positional_args(...): handles key="value", key={json}, and bare positional forms.
  • _parse_prefixed_call(...): NEW. Handles ASK: {...}, PROPOSE: {...}, Q: text, PLAN: text shapes that the trained 0.6B uses ~20% of the time.
• Root cause #2 — eval system prompt's example was being copied verbatim. Old eval SYSTEM_PROMPT hard-coded propose_plan(plan='{"stack": "python+fastapi", "scale": "1k users"}') as an example. Inspected the Qwen3-1.7B base eval (n=50) — 50/50 scenarios produced exactly {"stack": "python+fastapi", "scale": "1k users"} for event_planning and medical_intake tasks. The base model literally just copied the system-prompt example. Aligned eval SYSTEM_PROMPT character-for-character with train_grpo.py:PROMPT so trained model has zero distribution shift between train and eval.
• Re-launch plan: launched 3 fresh n50_v3 evals (0.6B-base, 1.7B-base, 0.6B-trained) with the prompt-aligned, prefix-tolerant parser. These are the first FAIR measurements of the trained-vs-untrained gap.
• Pre-fix table is misleading — the 0/50 scores reflect the eval bug, not the model's true behaviour. Trained 0.6B was scoring some non-zero rewards mid-trajectory (0.02–0.05 per question that revealed a field) before submitting a copied-example plan that scored 0. Post-fix numbers will be the headline.
• Files touched: inference.py (new _PREFIX_TO_TOOL table, _parse_prefixed_call, _find_balanced_func_call, _parse_positional_args, aligned SYSTEM_PROMPT); scripts/make_plots.py (multi-run --log-history/--eval support); scripts/poll_training.py (new robust monitor with retry-on-empty); scripts/refresh_all_plots.sh (new orchestrator).

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2026-04-25 23:25 IST — Cascade (Cursor) — Phase 8: run 1 evaluated end-to-end

• Run 1 finished: Qwen3-0.6B / 300 steps / a100-large / 38 min wall / pushed agarwalanu3103/clarify-rl-grpo-qwen3-0-6b to Hub. Reward grew 7× (0.006 → 0.045) over 300 steps — pipeline working.
• HF Inference Router refuses fine-tuned uploads (model_not_supported 400). Built scripts/eval_with_vllm.py + scripts/launch_eval_job.sh to host vLLM ourselves inside an HF Job (snapshot-downloads the project Space for inference.py + scenarios; runs scripts/run_eval.py against local vLLM; uploads results to model repo's evals/ folder).
• Qwen3 <think> token-waste fix: trained model burned full 300-token budget inside <think> blocks during eval, never reaching TOOL/ARGS. Patched inference.py to pass extra_body={"chat_template_kwargs": {"enable_thinking": False}} (matches train_grpo.py) and bumped MAX_TOKENS=800. Eval runtime dropped from never-completes → 33.6s for 50 scenarios.
• Headline numbers (run 1): trained avg=0.00 / format_pass=0% / 0 winners on 50 held-out, but avg-questions dropped to 3.32 (vs 5.06 for Qwen3-8B base, 5.87 for Qwen3-4B-Instruct, 4.0 for random policy). All untrained models also fail FormatCheck (best: 4B-Instruct 2/30 with max=0.81).
• Failure mode identified: trained 0.6B submits empty {} plans or wrong-family schemas ({"stack": ..., "scale": ...} for an event_planning task). The family→required-keys mapping wasn't learned in 300 steps — needs more steps or a bigger base.
• All 5 plots generated from real run-1 data (plots/01_..._05_..png). docs/trace_demo_run1.md written with 3 illustrative scenarios. outputs/baselines.json refreshed with comparative table. Total spend so far: $1.08 of $120 budget.
• Next: instant Anurag pastes HF_TOKEN_2/3 + GitHub URL → launch runs 2/3 (1.7B + 4B) in parallel; the larger bases are expected to clear FormatCheck.

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2026-04-25 22:00 IST — Cascade (Cursor) — Phase 7: smoke green + run 1 launched

• SMOKE TEST PASSED (job 69ece82ad70108f37acde9b8): Qwen3-0.6B / a10g-small / 5 steps / train_loss=-0.099 / 67s wall / model + Trackio bucket saved cleanly
• Resolved a 7-issue dependency cascade discovered by the smoke test (in chronological order):
  1. vllm_ascend — TRL unconditionally imports it on CUDA → monkey-patch importlib.util.find_spec to return None
  2. mergekit — TRL <1.0 eagerly imports it; pinned trl[vllm]>=1.0 in launcher to drop dependency entirely (mergekit also has incompatible pydantic constraints with vllm 0.10.2+)
  3. llm_blender — TRL eagerly imports; llm_blender itself imports TRANSFORMERS_CACHE (removed in transformers 5.x). Solution: stub via sys.modules + _BLOCKED_PACKAGES extension
  4. peft — TRL callbacks need PeftModel; added explicit --with peft
  5. transformers 5.x compatibility — keeping git+https://...transformers@main for vllm 0.17 + trl 1.0 cohabitation
  6. chat_template_kwargs only exists in trl >= 1.0 — added trl[vllm]>=1.0 pin AND defensive _grpo_kwargs filter via dataclasses.fields(GRPOConfig) so we survive future TRL upgrades
  7. Old llm_blender.__spec__ is None — extended monkey-patch to override transformers.utils.import_utils._is_package_available for blocked packages
• Defensive GRPOConfig init: _grpo_kwargs dict + filter against installed TRL's dataclass fields → drops unsupported kwargs with a warning instead of crashing
• macOS truststore: patched .venv/bin/hf entry-point to inject truststore.inject_into_ssl() for corporate proxy SSL (was blocking hf jobs logs)
• Production run 1 launched: Qwen3-0.6B / a100-large / 300 steps (job 69ece976d2c8bd8662bcdf48) on account 1 (agarwalanu3103); ETA ~50 min wall, ~$2.50
• docs/ANURAG_TODO.md rewritten: clean GUI-vs-terminal split for the user — they need to (A) provide HF tokens for accounts 2+3, (B) click Qwen3 license accept, (C) create a GitHub repo, (D) flip model cards to public after training. I handle everything else.
• Decisions locked this session: TRL pinned >=1.0 (drops mergekit need); flavor matrix updated to a100-large for 0.6B+1.7B and h200 for 4B; compute strategy unchanged (3 parallel runs, ~$20 projected)
• Next: monitor run 1 logs; await user to paste HF tokens 2+3 + GitHub URL; launch runs 2+3 + bake baseline evals while runs train

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2026-04-25 20:30 IST — Cascade (Cursor) — Phase 6: budget-unlocked rewrite

• User confirmed full $120 HF Jobs budget is available — strategy switched from "save money" to "buy reliability + quality"
• Root-cause fix for 0.000000 loss pathology: NUM_GENERATIONS=2 produces zero advantage when both rollouts agree on tokens (common early in training). Bumped default to auto-tune by GPU tier: 4/8/8 for 24GB/40GB/80GB.
• vllm_gpu_memory_utilization: raised from flat 0.40 → 0.55 on 40+ GB tiers. Was leaving half of A100/H100 idle.
• training/train_grpo.py upgrades: SMOKE_TEST mode, OOM trap, RESUME_FROM_CKPT, auto-resume from existing checkpoints, FAILED marker
• New scripts: scripts/launch_all.sh (parallel multi-account launcher), scripts/preflight.sh (concurrent WS probe), scripts/run_post_train_eval.sh (post-training eval orchestrator)
• Server change: SUPPORTS_CONCURRENT_SESSIONS=True + max_concurrent_envs=8 — enables 3-4 parallel HF Jobs against one Space
• Compute plan locked: 0.6B/a10g-large/500 + 1.7B/a100-large/400 + 4B/h100-large/250, ~$70 total; optional $25 insurance run on 1.7B seed=84
• Llama / Qwen2.5-Instruct rejected — chat template fails TRL add_response_schema; not retesting under time pressure
• Tokens received: agarwalanu3103, Kanan, mnit (3 accounts) — held in env vars only, not committed
• Doc updates: docs/11-submission-plan.md rewritten with phased rollout, docs/blog.md skeleton drafted with <PLACEHOLDER> for post-eval numbers, README.md refreshed with Colab badge + plot embeds + model card links
• Next: Anurag pushes server/ to Space (~5 min), runs preflight, runs smoke ($0.50), then launch_all.sh fires production runs

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2026-04-25 14:50 IST — Cascade (Windsurf) — Phase 4: deploy + baseline eval

• inference.py rewritten to submission format: OpenAI client, WebSocket env communication, [START]/[STEP]/[END] structured logs, BASELINE_MODE=policy/hybrid/llm, policy fallback
• HF Space deployed: agarwalanu3103/clarify-rl — Docker build, /health + /reset verified live
• Fixed Dockerfile (removed lockfile dependency), added .dockerignore, SUBMISSION_CHECKLIST.md, truststore SSL fix
• Policy baseline ran: all 3 tasks (easy/medium/hard) complete end-to-end, scores 0.00 (expected — empty plan). Per-question rewards confirmed (0.02-0.05)
• pyproject.toml updated: added openai, websockets deps
• Decisions locked this session: HF Space account = agarwalanu3103; inference uses OpenAI client (not huggingface_hub)
• Next: run hybrid/LLM baseline with HF credits, then start GRPO training

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2026-04-25 15:30 IST — Cascade (Windsurf) — gap analysis + regression tests

• Full gap analysis: cross-referenced specs 03/04/05 against all implementation code
• Fix: Added _guard_episode_done() — tool calls after episode ends now blocked (was unguarded)
• Fix: ask_question truncates to 200 chars per spec 03; 05-scenario-design.md difficulty table synced with code
• Tests: Created 4 test modules with 48 unit tests (all green locally): scenarios (reproducibility, field coverage, required keys), grader (parse_plan, rewards), user_simulator (keyword coverage, all-family reachability), plus integration test shells for env + rubrics
• Infra: __init__.py import-safe, root conftest.py, pytest config in pyproject.toml
• Decisions locked this session: none
• Next: Phase 4 — push to HF Space, write inference.py, generate eval set

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2026-04-25 13:50 IST — Cascade (Windsurf) — code audit + critical bug fixes

• Full audit of all server modules against design specs (scenarios, user_simulator, rubrics, grader, clarify_environment, app)
• Critical fix: Added step_async() override — reward/done/step_count was silently broken on WebSocket client path (training would have gotten no reward signal)
• Bug fix: Required keys now always included in profile for medium/hard; hard range adjusted (6,7) from (8,12) to match field pool sizes
• Added _patch_obs() helper + max_steps enforcement in env; oracle now scores 0.887 (was 0.0 due to FormatCheck gate failure)
• Created root README.md + .gitignore; updated 08-timeline.md (phases 1-3 done), 09-risks.md (R7 verified, R8 mitigated), 10-positioning.md
• Both smoke tests pass: smoke_env.py (direct) + smoke_client.py (WebSocket) with correct step_count=5 and reward propagation

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2026-04-25 12:05 IST — Cascade (Windsurf) — onboarding system + plan audit

• Audited ClarifyRL plan against all 6 official hackathon docs (FAQs, Help Guide, Themes, Travel Guide, Opening-Ceremony PDF, Resources)
• Patched 06-training-plan.md: HF Jobs t4-small primary path, fork TRL openenv_wordle_grpo.ipynb, sample-inspection cadence every 50 steps, LoRA save warning
• Patched 08-timeline.md: Setup phase adds hf auth login + hf jobs hardware; Phase 5 now "fork wordle notebook"; Phase 6 adds eyeball-4-rollouts step
• Patched 10-positioning.md: added "Direct alignment with judges' own example ideas" section citing slide 50 ("Realistic customer engagement / frustrated customers" → our support_triage family)
• Created multi-session handoff system: .windsurf/rules/clarify-rl.md (auto-loaded), docs/AGENT_ONBOARDING.md (manual paste), docs/STATUS.md (live state), docs/SESSION_LOG.md (this file)
• Decisions locked this session: HF Jobs is primary compute (was Colab); starter notebook is openenv_wordle_grpo.ipynb
• Next: implement server/scenarios.py per docs/05-scenario-design.md

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2026-04-25 ~11:30 IST — Cascade (Windsurf) — positioning sharpening

• Reframed pitch from "personal-assistant booking demo" to "AI safety / hallucination" framing
• Rewrote 00-overview.md to lead with Air Canada / lawyer / Cursor anchors
• Expanded task families from 5 personal to 3 high-stakes + 2 personal: coding_requirements, medical_intake, support_triage, meeting_scheduling, event_planning
• Promoted hallucination rate (90% → 3%) to headline metric, demoted plan satisfaction to secondary
• Created 10-positioning.md with safety/alignment framing for judges
• Updated 01-requirements.md F2.2 / F6.3 to match new family names + added hallucination acceptance criterion
• Decisions locked this session: Wild Card #5 as primary theme pitch; new 5-family split; hallucination as headline metric

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Pre-2026-04-25 — design + scaffolding (multiple sessions, summarized)

• Locked idea: ClarifyRL / AskBeforeYouAct (train LLM to ask before acting)
• Created design doc set 00-09 in clarify-rl/docs/
• Set up scaffolding: pyproject.toml, Dockerfile, openenv.yaml, models.py, client.py, server/__init__.py
• Confirmed stack: OpenEnv 0.2.2 + MCPEnvironment + Unsloth + TRL GRPO + Qwen2.5-1.5B-Instruct
• Confirmed compute: free Colab T4 + M3 Pro 18GB (later updated to add HF Jobs as primary)
• Confirmed team: Bhole Chature (Anurag + Kanan)

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Entry template (copy-paste for new sessions)

## YYYY-MM-DD HH:MM IST — <agent / chat tag> — <one-line summary>

- Did: <bullet>
- Did: <bullet>
- Did: <bullet>
- Decisions locked this session: <or "none">
- Next: <what the next agent should pick up>

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