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Tighten README: resolve GRPO contradiction, drop duplicate baseline table, remove internal mentor docs
Three structural fixes after a structural review of the submission docs: 1. GRPO contradiction (the worst issue): the README's headline section said GRPO refine 'lifted OOD by +0.023' while the Training section later said 'Stage 3 — Skipped in our headline result'. Both were simultaneously visible to a careful reader. Rewrote Stage 3 to say what actually happened: GRPO refine ran on top of SFT, lifted OOD generalization by +0.023 and discrete-3 by +0.013, no in-dist regression. Added the actual submission command. 2. Duplicate 'Baseline Scores (v2 grader)' table near line 255 had: - 'TBD' for the OOD teacher (which was 0.621 in the headline) - '(see results.md)' for the distilled student (which was already in the headline table) Pure noise; deleted. Headline table is the canonical reference. 3. Removed two docs that were internal references, not for judges: - docs/WhatMAkesSubmissionStandOut.md (mentor's stand-out criteria pasted in as a check…0503beb
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Algorithm Distillation: grader v2 with belief_accuracy + SFT pipeline
Grader changes: - Add belief_accuracy term (weight 0.20) to _grade_episode - env.record_belief() for callers to register the agent's emitted belief - Redistribute weights: crash 0.20→0.15, progress 0.25→0.20, conn 0.15→0.10, adapt 0.30→0.25 - Heuristic + random baselines still emit no belief, score 0 on this component - Validates: gpt-5.4 teacher beats heuristic 0.617 vs 0.449 on 30/30 episodes Distillation pipeline: - scripts/generate_teacher_trajectories.py: Azure OpenAI teacher rollouts - scripts/reeval_teacher_trajectories.py: offline re-score under v2 grader - scripts/upload_teacher_data.py: push trajectories to HF Hub dataset - training/sft_prime.py: TRL SFTTrainer fine-tune of Qwen 2.5-3B on teacher data - scripts/sft_on_hf.py: HF Jobs orchestrator for SFT prime stage Parser + prompt: - extract_action_and_belief now handles both single-line and CoT-prefixed formats (takes LAST <digit> <digit> <digit> <ACTION> match) - SYSTEM_PROMPT in dataset.py unified with teacher's CoT-as…ece0bbe
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docs: add sim-to-real deployment architecture reference
Captures the real-world product pipeline: wearable proxies for meters, calendar-to-action-type mapping, Accept/Ignore as reward signal. Also covers why the frozen model adapts in-context and why RL pretraining enables fast personalization (MAML analogy). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>24adee5 -
docs: reorganize — 25 files → 4 focused docs
Deleted all brainstorming (Plan_v2), Round 1 specs, stale Round 2 docs, and research references that no longer serve the submission. Final structure: docs/blog_post.md — public narrative (product vision + training story) docs/entity_definitions.md — technical reference (state/actions/profiles/reward) docs/environment_design.md — design rationale (why these choices, training arc) docs/training.md — training guide (GRPO config, pipeline, results) docs/references/judging_criteria.md — external hackathon criteria Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>1a25a1a
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Rebuild as Life Simulator: 5 meters, 3 hidden profiles, GRPO training pipeline
server/rhythm_environment.py: full Life Simulator (28 steps, 5 meters, 10 actions, 3 hidden personality profiles with distinct reward weights and action modifiers) models.py: add reward/done fields to RhythmObservation training/: complete GRPO pipeline (dataset, train, reward_functions, inference_eval, Colab notebook); env_reward uses real episode replay via dataset columns ui/app.py: full rewrite for Life Simulator (5-meter display, 10 actions, profile inspector) blog_post.md: mini-blog for hackathon submission docs/round2/: entity_definitions and environment_design updated to Life Simulator spec Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>cc6473a