InosLihka commited on
Commit
1ba0d0e
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1 Parent(s): 1217c1d

Prune internal/stale docs; sharpen README submission links

Browse files

Removed (internal-only or stale, not for judges):
- docs/SubmissionFormDump.md (form notes)
- docs/handoff_for_next_session.md (chat-handoff doc)
- docs/iter4_partial_analysis.txt (mid-iteration analysis)
- eval_baselines_meta.json (v1 grader baselines, replaced by v2)
- eval_results.json (v1 single-task eval, superseded)
- training_log.json (v1 GRPO log, superseded by per-iter HF Hub model repos)

README:
- Renamed 'Links' section to 'Submission links (for judges)' with explicit
callouts for HF Space URL, training notebook, blog post — the 3 URLs
judges need from the form

README.md CHANGED
@@ -15,14 +15,15 @@ An OpenEnv environment where an LLM agent learns *how to learn a person*. Each e
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  This is **meta-reinforcement learning** for personalization: the agent isn't trained to optimize one person's life, it's trained to acquire the *skill of figuring out a new person* from a handful of interactions.
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- ## Links
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-
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- - **Live Environment**: [HF Space — InosLihka/rhythm_env](https://huggingface.co/spaces/InosLihka/rhythm_env)
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- - **Trained Model (Algorithm Distillation)**: [InosLihka/rhythm-env-meta-trained-sft-v1](https://huggingface.co/InosLihka/rhythm-env-meta-trained-sft-v1)
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- - **Teacher Trajectories Dataset**: [InosLihka/rhythm-env-teacher-trajectories](https://huggingface.co/datasets/InosLihka/rhythm-env-teacher-trajectories)
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- - **Headline Results**: [docs/results.md](docs/results.md)
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- - **Blog Post**: [Teaching an AI to Know You (Without Asking)](docs/blog_post.md)
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- - **Training Notebook**: [Colab — GRPO Training](training/RhythmEnv_GRPO_Training.ipynb)
 
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  ## Why a Life Simulator?
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  This is **meta-reinforcement learning** for personalization: the agent isn't trained to optimize one person's life, it's trained to acquire the *skill of figuring out a new person* from a handful of interactions.
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+ ## Submission links (for judges)
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+
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+ - **HF Space (the environment)**: https://huggingface.co/spaces/InosLihka/rhythm_env
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+ - **Training notebook**: [training/RhythmEnv_GRPO_Training.ipynb](training/RhythmEnv_GRPO_Training.ipynb)
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+ - **Blog post**: [docs/blog_post.md](docs/blog_post.md) — *Teaching an AI to Know You (Without Asking)*
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+ - **Headline results**: [docs/results.md](docs/results.md)
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+ - **Trained model (Algorithm Distillation)**: https://huggingface.co/InosLihka/rhythm-env-meta-trained-sft-v1
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+ - **Teacher trajectories dataset**: https://huggingface.co/datasets/InosLihka/rhythm-env-teacher-trajectories
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+ - **Iteration journey + lessons**: [docs/iterations.md](docs/iterations.md)
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  ## Why a Life Simulator?
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docs/handoff_for_next_session.md DELETED
@@ -1,224 +0,0 @@
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- # Handoff for new chat session
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-
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- Paste the contents of this file (or just point the new chat at it) to bring
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- a fresh Claude session up to speed.
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-
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- ---
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-
8
- ## Project + vision
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-
10
- **Meta OpenEnv Hackathon submission** — `RhythmEnv`, a meta-RL environment
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- where an LLM agent learns the *skill of inferring a user's hidden personality*
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- from observation alone. POC trains in simulation; production replaces the
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- synthetic meters with real wearable signals (HRV, calendar, accept/ignore taps).
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-
15
- **The aim** is the agent should require ~no explicit input from users — works
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- purely from passive sensor data + tap responses. SensorLM (Google, 2025) is
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- the proven input layer for production.
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-
19
- ## Where the code lives
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-
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- - Local: `c:/Users/guptapri/Downloads/Akhil/Repos/hackathon/rhythm_env/`
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- - HF Space (deployed): `https://huggingface.co/spaces/InosLihka/rhythm_env`
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- - Git remote: `hf` → that HF Space (main branch). Local working branch: `round2`.
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- - HF token: `~/.cache/huggingface/token`
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-
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- ## Current state — iteration 5 running
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-
28
- | iter | Hardware | Config | Result |
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- |---|---|---|---|
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- | 1 | a100 | 200 steps, LoRA 4, num_gen 4 | Mode collapse → `EXERCISE 5 5 5` |
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- | 2 | a100 | 400 steps + 7 fixes (temp 1.5, weights, action_legal=0, repetition penalty, late_quality math, hint=0, seed-mix) | Mode collapse → MEDITATE-EXERCISE 2-cycle |
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- | 3 | n/a | 800 steps + 7 fixes | Cancelled before run (stale code) |
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- | 4 (a100/l40s/h200 attempts) | various | various | Capacity-cancelled or H200/Unsloth incompat |
34
- | **4 (a10g)** | a10g-large | LoRA 16, num_gen 8, 800 steps + further 7 fixes from external bug review | **CANCELLED at step 235 by mistake** based on stale API logs. UI showed it was healthy. ~$2.10 wasted. |
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- | **5 (a10g)** | a10g-large | LoRA 8, num_gen 4, 500 steps + same 7 fixes as iter 4 | **COMPLETED** but HF marked ERROR (job timeout AFTER upload, same iter-2 pattern). Trained model + plots + eval at `huggingface.co/InosLihka/rhythm-env-meta-trained-iter5` |
36
-
37
- **Spend: ~$7.10 of $30 budget.**
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-
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- ## ITER 5 VERDICT (the data we have)
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-
41
- | Condition | iter1 | iter2 | **iter5** | Heuristic | Random |
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- |---|---|---|---|---|---|
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- | Continuous in-dist | 0.224 | 0.224 | **0.349** | **0.587** | 0.520 |
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- | Continuous OOD | 0.219 | 0.219 | **0.331** | **0.576** | 0.489 |
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- | Discrete-3 | 0.232 | 0.217 | **0.339** | **0.579** | 0.547 |
46
-
47
- Iter 5 is BETTER than iter 1+2 (+50%) but still ~40% below heuristic.
48
-
49
- **Action distribution iter 5**: 86% SLEEP, 11.7% LEARN, 7 actions used.
50
- Third distinct mode collapse pattern (after iter1 EXERCISE collapse and iter2
51
- MEDITATE-EXERCISE 2-cycle collapse).
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-
53
- **Belief output iter 5**: ALL 14 eval episodes emit constant `(0.5, 0.5, 0.5)`.
54
- The belief baseline subtraction fix removed the free-reward attractor but
55
- didn't create incentive for real learning. The agent emits the math-optimal
56
- "give up" belief.
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-
58
- **Belief MAE iter 5**: 0.110 in-dist (looks great!) — but only because
59
- constant (0.5, 0.5, 0.5) accidentally matches the average true profile under
60
- the new tighter profile cap. MEASUREMENT ARTIFACT, NOT LEARNING.
61
-
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- ## The whack-a-mole pattern — the structural issue
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-
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- | Iter | Fix | New collapse mode |
65
- |---|---|---|
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- | 1 | none | EXERCISE single-action |
67
- | 2 | repetition penalty | MEDITATE-EXERCISE 2-cycle |
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- | 5 | belief baseline + cycle penalty + grader bias | SLEEP-spam + neutral belief |
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-
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- Each fix closes one loophole and opens another. **The unaddressed structural
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- issue is per-step GRPO vs per-episode grader misalignment** — flagged by the
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- external agent as "multi-step plan generation" recommendation but never
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- implemented. That's the single highest-leverage missing fix.
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-
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- ## The two rounds of fixes applied
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-
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- ### Round 1 (after iter 1 collapse, applied for iter 2): the 7 hyperparameter/reward fixes
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- 1. `temperature` 1.0 → 1.5 (force diverse rollouts)
79
- 2. `reward_weights` `[0.3, 0.3, 1.0, 1.0]` → `[0.05, 0.05, 1.5, 3.0]` (suppress saturated layers)
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- 3. `action_legal` returns 0 (was +0.5) for valid — drop the constant-reward layer
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- 4. Explicit repetition penalty in env_reward (-0.3 if action 3+ in row)
82
- 5. `_grade_episode` `late_quality` normalization fix ([-3, +3] not [-1, +1])
83
- 6. `hint_fraction` 0.15 → 0.0 (eliminate train-eval distribution mismatch)
84
- 7. `env_reward` seed-fallback hardening (`(i*17)^0xBEEF` mix to break clusters)
85
-
86
- ### Round 2 (after iter 2 collapse + external bug review, applied for iter 4 onward): 7 deeper fixes
87
- 1. **Anomalies surfaced in prompt** (StepRecord + format_observation_prompt + inference.py) — was computed but never visible to agent
88
- 2. **Belief baseline subtraction** (`belief_accuracy`): reward = similarity − constant_baseline_similarity, so constant `5 5 5` no longer earns +1/step free reward
89
- 3. **Profile weight cap 0.80 → 0.45** (`sample_profile`) — forces multi-meter profiles
90
- 4. **Scaled-down shaping**: -0.10/-0.15/+0.07 (was -0.30/-0.40/+0.20)
91
- 5. **Step-0 belief reward = 0** (no info to commit on)
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- 6. **Belief-action coupling reward**: ±0.15 if action matches/contradicts emitted belief
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- 7. **`grader_bias` moved out of `_compute_reward` into `env_reward`** — keeps env per-step reward pure for inference signal
94
-
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- ## What iter 4 partial data (235/800 steps) tells us
96
-
97
- Logs at `docs/logdump.txt`. Analysis at `docs/iter4_partial_analysis.txt`.
98
-
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- **Working:**
100
- - Total reward: -3.4 → +0.39 (climbing)
101
- - format_valid: -1.20 → +0.44 (slow but climbing)
102
- - env_reward: -2.01 → +0.44 (climbing)
103
- - grad_norm normalized to ~10
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- - No catastrophic mode collapse
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-
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- **Still broken — the unsolved core:**
107
- - `belief_accuracy/mean` flat at **-0.10** throughout 235 steps
108
- - Linear slope: +0.0007 per 100 steps (essentially zero)
109
- - Agent emits beliefs SLIGHTLY WORSE than constant baseline
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-
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- **Root-cause hypothesis** (from analysis):
112
-
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- The profile cap (0.80 → 0.45) compressed the belief target distribution.
114
- With balanced profiles, sampled belief vectors land near `[0.5, 0.5, 0.5]`, so
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- the constant `5 5 5` baseline already has high similarity. Real learning has
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- a tiny ceiling. **Two fixes (profile cap + baseline subtraction) interact
117
- negatively.**
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-
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- ## Pending fixes NOT yet attempted (priority order for iter 6+)
120
-
121
- 1. **HIGHEST PRIORITY: revert profile cap to 0.80** — restores belief target
122
- spread; the new `grader_bias` term handles the original "spam recovery
123
- actions" exploit independently. Single-line fix in `sample_profile`.
124
-
125
- 2. **Multi-step plan generation** — deferred from external agent's analysis.
126
- Completion = 3-action plan instead of 1 action; `env_reward` replays the
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- plan cumulatively. Addresses the structural per-step-GRPO vs per-episode-
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- grader mismatch (`adaptation_score` is 30% of grade but only ~3% of training
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- rows see it via terminal bonus). This is the highest-leverage missing fix.
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-
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- 3. **Captioned anomaly history** — describe per-meter anomalies in language
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- ("vitality dropped 5% MORE than baseline") instead of `[anom V-0.05]`.
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- Don't bake in conclusions ("this person is introverted") — just describe.
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-
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- ## What I would do differently next time (the methodology lesson)
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-
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- The user (Akhil) explicitly called this out 2026-04-26: "I don't want to do
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- hit and trial." Each iter exposed a bug we should have caught upfront.
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-
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- Things that would have prevented the wasted iters:
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- - **Compute reward variance across IDENTICAL completions** before training.
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- GRPO advantage = reward − group_mean; if all 4 completions get the same
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- reward from a layer, that layer contributes ZERO gradient. `pipeline_dryrun`
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- tested DIFFERENT actions per kind, missing this.
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- - **Analytically check belief target distribution** under continuous profiles
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- before tightening the cap. Would have caught the iter 4 issue.
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- - **Run a 50-step micro-smoke-train ($0.10)** on the smallest possible config
148
- before committing to 200+ step runs.
149
- - **Address structural issues (multi-step plans) before tweaking hyperparams.**
150
-
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- ## Key files for new session
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-
153
- | File | What it has |
154
- |---|---|
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- | `docs/architecture.md` | 10 ASCII diagrams with concrete values, full pipeline |
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- | `docs/iterations.md` | Iter 0-3 journey doc (NEEDS update for iter 4+5) |
157
- | `docs/logdump.txt` | Iter 4 raw UI logs through step 235 (3272 lines) |
158
- | `docs/iter4_partial_analysis.txt` | Parsed iter 4 trajectory snapshots + trends |
159
- | `docs/results.md` | Template for headline results (NOT yet filled) |
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- | `docs/blog_post.md` | Strong narrative, pre-meta-RL refactor |
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- | `docs/references/judging_criteria.md` | Hackathon scoring (40% innovation, 30% storytelling, 20% improvement, 10% reward pipeline) |
162
- | `training/WhatMakesAGoodSubmission.md` | Hackathon criteria, mentions OpenEnv Rubric system (we don't use it — gap) |
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- | `server/rhythm_environment.py` | Env, sample_profile, _compute_reward, _grade_episode |
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- | `training/reward_functions.py` | 4-layer reward stack, parser, belief_accuracy |
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- | `training/dataset.py` | Prompt builder, episode generator |
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- | `training/train.py` | GRPOConfig setup, reward_weights wiring |
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- | `scripts/train_on_hf.py` | HF Jobs orchestrator |
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- | `scripts/analyze_logdump.py` | Parse iter 4 UI export |
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- | `scripts/analyze_iter.py` | Pull + analyze any iter's HF Hub repo |
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- | `eval_baselines_meta.json` | Heuristic in-dist 0.587, OOD 0.580 — bars to beat |
171
-
172
- ## Open monitor + job state at handoff
173
-
174
- - **Active monitor**: task `b10lozxd8` watching iter 5
175
- - **Iter 5 job**: `69eda027d70108f37acdf9a7` on a10g-large, RUNNING
176
- - **HF Job UI**: `https://huggingface.co/jobs/InosLihka/69eda027d70108f37acdf9a7`
177
- - **Iter 5 model repo (when complete)**: `https://huggingface.co/InosLihka/rhythm-env-meta-trained-iter5`
178
-
179
- ## What to do in new session
180
-
181
- **If iter 5 results are landed:**
182
- 1. Check `docs/logdump.txt` for the new logs OR pull from `InosLihka/rhythm-env-meta-trained-iter5`
183
- 2. Run `python scripts/analyze_iter.py iter5`
184
- 3. Decide: ship it / iter 6 with profile-cap revert / iter 6 with multi-step plans
185
-
186
- **If iter 5 is still running:**
187
- 1. Check status via `https://huggingface.co/jobs/InosLihka/69eda027d70108f37acdf9a7`
188
- 2. Trust the UI, NOT the API (lag is severe)
189
-
190
- **If you're starting fresh after iter 5 completed:**
191
- 1. Read this file
192
- 2. Check the iter 5 model repo
193
- 3. Decide path forward based on the belief_accuracy outcome
194
-
195
- ## Commands you'll need
196
-
197
- ```bash
198
- # HF Jobs status
199
- hf jobs ps
200
- hf jobs inspect <id>
201
- hf jobs logs <id> # use UI instead — API lags
202
-
203
- # Submit a new training run
204
- cd c:/Users/guptapri/Downloads/Akhil/Repos/hackathon/rhythm_env
205
- hf jobs uv run --flavor a10g-large --secrets HF_TOKEN \
206
- -e FAST_MODE=1 \
207
- -e MODEL_REPO_SUFFIX=iter6 \
208
- -e LORA_RANK=8 -e NUM_GENERATIONS=4 -e MAX_STEPS=500 \
209
- -d scripts/train_on_hf.py
210
-
211
- # Pull and analyze a finished iter
212
- python scripts/analyze_iter.py iter5
213
-
214
- # Tests still pass:
215
- python -m pytest tests/ -q
216
- ```
217
-
218
- ## Hardware notes (learned the hard way)
219
-
220
- - **a100-large**: best perf but capacity-limited at peak hours
221
- - **a10g-large**: reliable, ~30% slower, well-tested with Unsloth
222
- - **l40sx1**: also capacity-limited
223
- - **h200**: Unsloth doesn't detect the GPU (sm_90 incompat) — DO NOT USE
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- - **HF Jobs API `/logs` endpoint lags severely** — always cross-check via the live UI
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
docs/iter4_partial_analysis.txt DELETED
@@ -1,41 +0,0 @@
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- Parsed 241 metric rows
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-
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- metric step~ 0 step~ 30 step~ 60 step~ 120 step~ 180 step~ 240
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- ------------------------------------------------------------------------------------
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- loss -0.000 +0.007 +0.056 +0.081 +0.058 +0.036
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- reward -3.189 -3.726 -0.771 -0.125 +0.928 +0.937
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- reward_std +2.168 +2.272 +1.452 +0.691 +0.099 +0.193
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- frac_zero_std +0.000 +0.000 +0.000 +0.250 +0.250 +0.000
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- format_valid -1.125 -1.375 +0.375 +0.344 +0.672 +0.688
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- action_legal -0.656 -0.750 -0.125 -0.062 +0.000 +0.000
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- env_reward -1.875 -2.213 -0.348 +0.107 +0.759 +0.812
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- belief_accuracy -0.096 -0.100 -0.087 -0.100 -0.081 -0.105
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- kl -0.000 +0.171 +1.398 +2.033 +1.445 +0.903
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- compl_length +32.000 +32.000 +32.000 +32.000 +32.000 +32.000
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- grad_norm +36.083 +59.430 +5.959 +1.448 +1.559 +9.748
16
-
17
- === Linear trend (slope) over the run � units per 100 steps ===
18
- loss slope/100steps=+0.0163 first20-mean=+0.005 last20-mean=+0.040 delta=+0.035 [UP]
19
- reward slope/100steps=+1.7239 first20-mean=-3.400 last20-mean=+0.390 delta=+3.791 [UP]
20
- reward_std slope/100steps=-0.7742 first20-mean=+1.554 last20-mean=+0.334 delta=-1.220 [DOWN]
21
- frac_zero_std slope/100steps=+0.2577 first20-mean=+0.163 last20-mean=+0.388 delta=+0.225 [UP]
22
- format_valid slope/100steps=+0.6736 first20-mean=-1.195 last20-mean=+0.438 delta=+1.633 [UP]
23
- action_legal slope/100steps=+0.2732 first20-mean=-0.702 last20-mean=-0.056 delta=+0.645 [UP]
24
- env_reward slope/100steps=+1.1163 first20-mean=-2.013 last20-mean=+0.438 delta=+2.451 [UP]
25
- belief_accuracy slope/100steps=+0.0007 first20-mean=-0.095 last20-mean=-0.095 delta=+0.000 [FLAT]
26
- kl slope/100steps=+0.4085 first20-mean=+0.123 last20-mean=+0.995 delta=+0.872 [UP]
27
- compl_length slope/100steps=-0.0297 first20-mean=+32.000 last20-mean=+32.000 delta=+0.000 [FLAT]
28
- grad_norm slope/100steps=-474.6836 first20-mean=+2388.734 last20-mean=+11.383 delta=-2377.351 [DOWN]
29
-
30
- === Mode-collapse warning signs ===
31
- Last-50 mean frac_reward_zero_std: 0.56 (1.0 = full collapse)
32
- Traceback (most recent call last):
33
- File "C:\Users\guptapri\Downloads\Akhil\Repos\hackathon\rhythm_env\scripts\analyze_logdump.py", line 124, in <module>
34
- main()
35
- File "C:\Users\guptapri\Downloads\Akhil\Repos\hackathon\rhythm_env\scripts\analyze_logdump.py", line 105, in main
36
- print(f"Last-50 mean reward_std: {avg_reward_std:.3f} (\u22650.3 = healthy variance)")
37
- File "C:\Users\guptapri\AppData\Local\Programs\Python\Python312\Lib\encodings\cp1252.py", line 19, in encode
38
- return codecs.charmap_encode(input,self.errors,encoding_table)[0]
39
- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
40
- UnicodeEncodeError: 'charmap' codec can't encode character '\u2265' in position 43: character maps to <undefined>
41
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eval_baselines_meta.json DELETED
@@ -1,3642 +0,0 @@
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- [
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- {
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- "condition": "discrete-3-profiles (legacy)",
4
- "profile_name": "introvert_morning",
5
- "profile_mode": "discrete",
6
- "strategy": "heuristic",
7
- "seed": 0,
8
- "final_score": 0.5745,
9
- "total_reward": 5.84,
10
- "adaptation": -0.28,
11
- "vitality": 0.76,
12
- "cognition": 0.41,
13
- "progress": 1.0,
14
- "serenity": 1.0,
15
- "connection": 0.43,
16
- "actions": [
17
- "deep_work",
18
- "admin_work",
19
- "meditate",
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- "sleep",
21
- "deep_work",
22
- "admin_work",
23
- "meditate",
24
- "sleep",
25
- "deep_work",
26
- "admin_work",
27
- "meditate",
28
- "sleep",
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- "deep_work",
30
- "admin_work",
31
- "socialize",
32
- "sleep",
33
- "deep_work",
34
- "admin_work",
35
- "meditate",
36
- "sleep",
37
- "deep_work",
38
- "admin_work",
39
- "socialize",
40
- "sleep",
41
- "deep_work",
42
- "admin_work",
43
- "meditate",
44
- "sleep"
45
- ],
46
- "true_belief": [
47
- 0.0,
48
- 1.0,
49
- 0.069
50
- ],
51
- "final_belief": null,
52
- "belief_mae": null
53
- },
54
- {
55
- "condition": "discrete-3-profiles (legacy)",
56
- "profile_name": "introvert_morning",
57
- "profile_mode": "discrete",
58
- "strategy": "heuristic",
59
- "seed": 1,
60
- "final_score": 0.5765,
61
- "total_reward": 6.88,
62
- "adaptation": -0.142,
63
- "vitality": 0.64,
64
- "cognition": 0.33,
65
- "progress": 1.0,
66
- "serenity": 0.87,
67
- "connection": 0.43,
68
- "actions": [
69
- "deep_work",
70
- "admin_work",
71
- "meditate",
72
- "sleep",
73
- "deep_work",
74
- "admin_work",
75
- "meditate",
76
- "sleep",
77
- "deep_work",
78
- "admin_work",
79
- "meditate",
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The diff for this file is too large to render. See raw diff