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Add diminishing returns diagnostic report

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2026-07-05 18:32:47 +09:00
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docs/reports/diminishing-returns-2026-07-05-expert.png
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docs/reports/diminishing-returns-2026-07-05-summary.jsonl
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{"current": "league_v1_update_250", "current_label": "league v1 update 250", "event": "h2h_adjacent", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/h2h_league_v1_update_250_vs_ladder_v2_gate3.json", "max_steps_rate": 0.0, "mean_game_length": 57.4935, "mean_score_diff": 42.87575, "opened_colors_per_game": 4.911, "play_action_rate": 0.6949448449578737, "positive_expeditions_per_game": 2.56825, "previous": "ladder_v2_gate3", "previous_label": "ladder v2 gate3", "score_diff_ci95_high": 44.43765342421665, "score_diff_ci95_low": 41.313846575783344, "score_diff_std": 50.40064352917072, "wilson_high": 0.8135690832007875, "wilson_low": 0.7888523724955226, "win_rate": 0.8015}
{"current": "league_v1_update_500", "current_label": "league v1 update 500", "event": "h2h_adjacent", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/h2h_league_v1_update_500_vs_league_v1_update_250.json", "max_steps_rate": 0.0, "mean_game_length": 50.7755, "mean_score_diff": 5.20975, "opened_colors_per_game": 4.9415, "play_action_rate": 0.7485131356099098, "positive_expeditions_per_game": 2.351, "previous": "league_v1_update_250", "previous_label": "league v1 update 250", "score_diff_ci95_high": 6.664905283640598, "score_diff_ci95_low": 3.7545947163594007, "score_diff_std": 46.95601635366284, "wilson_high": 0.5558977420904201, "wilson_low": 0.525024543003114, "win_rate": 0.5405}
{"current": "repair_c01_update_500", "current_label": "repair c01 update 500", "event": "h2h_adjacent", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/h2h_repair_c01_update_500_vs_league_v1_update_500.json", "max_steps_rate": 0.0, "mean_game_length": 49.2915, "mean_score_diff": 3.64125, "opened_colors_per_game": 4.9395, "play_action_rate": 0.7722021038110018, "positive_expeditions_per_game": 2.43225, "previous": "league_v1_update_500", "previous_label": "league v1 update 500", "score_diff_ci95_high": 5.0464015232057395, "score_diff_ci95_low": 2.23609847679426, "score_diff_std": 45.34245839244679, "wilson_high": 0.5476742803350019, "wilson_low": 0.5167638355728124, "win_rate": 0.53225}
{"checkpoint": "ladder_v2_gate3", "event": "expert_anchor", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/expert_anchor_ladder_v2_gate3.json", "label": "ladder v2 gate3", "max_steps_rate": 0.0, "mean_game_length": 61.398, "mean_score_diff": 43.456, "opened_colors_per_game": 4.55925, "play_action_rate": 0.6680377805945128, "positive_expeditions_per_game": 2.61475, "score_diff_ci95_high": 44.81720694691559, "score_diff_ci95_low": 42.094793053084416, "score_diff_std": 43.92442262256479, "stage_index": 0, "wilson_high": 0.8490930348441887, "wilson_low": 0.8262583814919731, "win_rate": 0.838}
{"checkpoint": "league_v1_update_250", "event": "expert_anchor", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/expert_anchor_league_v1_update_250.json", "label": "league v1 update 250", "max_steps_rate": 0.0, "mean_game_length": 56.52425, "mean_score_diff": 26.42325, "opened_colors_per_game": 4.54775, "play_action_rate": 0.6422064732439839, "positive_expeditions_per_game": 2.19725, "score_diff_ci95_high": 27.654455989944754, "score_diff_ci95_low": 25.192044010055245, "score_diff_std": 39.729456538778834, "stage_index": 1, "wilson_high": 0.7570405983993863, "wilson_low": 0.7299916729968844, "win_rate": 0.74375}
{"checkpoint": "league_v1_update_500", "event": "expert_anchor", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/expert_anchor_league_v1_update_500.json", "label": "league v1 update 500", "max_steps_rate": 0.0, "mean_game_length": 53.29175, "mean_score_diff": 13.967, "opened_colors_per_game": 4.84725, "play_action_rate": 0.6934619506966774, "positive_expeditions_per_game": 2.14075, "score_diff_ci95_high": 15.054322005906956, "score_diff_ci95_low": 12.879677994093045, "score_diff_std": 35.086502770569304, "stage_index": 2, "wilson_high": 0.6609204517986065, "wilson_low": 0.6312989110391558, "win_rate": 0.64625}
{"checkpoint": "repair_c01_update_500", "event": "expert_anchor", "games": 4000, "json": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05/expert_anchor_repair_c01_update_500.json", "label": "repair c01 update 500", "max_steps_rate": 0.0, "mean_game_length": 54.61975, "mean_score_diff": 19.654, "opened_colors_per_game": 4.7015, "play_action_rate": 0.6708126417051431, "positive_expeditions_per_game": 2.1505, "score_diff_ci95_high": 20.81558482366198, "score_diff_ci95_low": 18.49241517633802, "score_diff_std": 37.482869759149246, "stage_index": 3, "wilson_high": 0.7068566468456556, "wilson_low": 0.6782734877661645, "win_rate": 0.69275}
{"budget": "same-budget phase-1 exploiter", "delta_from_previous_same_protocol": null, "event": "exploiter_trajectory", "mean_score_diff": 54.7721, "phase": "ladder_v2", "protocol": "short_random_shaping", "source": "docs/reports/ladder-v2-2026-07-05-summary.jsonl", "target": "ladder_v2_gate3", "warmstart": "random", "win_rate": 0.83135}
{"budget": "league v1 cycle exploiter", "delta_from_previous_same_protocol": null, "event": "exploiter_trajectory", "mean_score_diff": 0.6818, "phase": "league_v1", "protocol": "league_cycle_exploiter", "source": "docs/reports/league-v1-2026-07-05-summary.jsonl", "target": "league_v1_update_250", "warmstart": "random", "win_rate": 0.50225}
{"budget": "same exploiter vs final", "delta_from_previous_same_protocol": -0.031299999999999994, "event": "exploiter_trajectory", "mean_score_diff": -2.63775, "phase": "league_v1", "protocol": "league_cycle_exploiter", "source": "docs/reports/league-v1-2026-07-05-summary.jsonl", "target": "league_v1_update_500", "warmstart": "random", "win_rate": 0.47095}
{"budget": "1200 updates, shaping anneal", "delta_from_previous_same_protocol": null, "event": "exploiter_trajectory", "mean_score_diff": 10.95125, "phase": "gates_1_2_original", "protocol": "long_random_shaping", "source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl", "target": "league_v1_update_500", "warmstart": "random", "win_rate": 0.587}
{"budget": "900 updates, shaping 0", "delta_from_previous_same_protocol": null, "event": "exploiter_trajectory", "mean_score_diff": 11.38075, "phase": "gates_1_2_original", "protocol": "warmstart_gate3_no_shaping", "source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl", "target": "league_v1_update_500", "warmstart": "ladder_v2_gate3", "win_rate": 0.59}
{"budget": "900 updates, shaping 0", "delta_from_previous_same_protocol": null, "event": "exploiter_trajectory", "mean_score_diff": 10.42525, "phase": "gates_1_2_original", "protocol": "replay_exploiter_no_shaping", "source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl", "target": "league_v1_update_500", "warmstart": "league_v1_cycle_1_exploiter", "win_rate": 0.582}
{"budget": "1200 updates, shaping anneal", "delta_from_previous_same_protocol": -0.04799999999999993, "event": "exploiter_trajectory", "mean_score_diff": 6.27925, "phase": "repair_c01", "protocol": "long_random_shaping", "source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl", "target": "repair_c01_update_500", "warmstart": "random", "win_rate": 0.539}
{"budget": "900 updates, shaping 0", "delta_from_previous_same_protocol": -0.040999999999999925, "event": "exploiter_trajectory", "mean_score_diff": 7.0285, "phase": "repair_c01", "protocol": "warmstart_gate3_no_shaping", "source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl", "target": "repair_c01_update_500", "warmstart": "ladder_v2_gate3", "win_rate": 0.549}
{"budget": "900 updates, shaping 0", "delta_from_previous_same_protocol": -0.042749999999999955, "event": "exploiter_trajectory", "mean_score_diff": 6.10225, "phase": "repair_c01", "protocol": "replay_exploiter_no_shaping", "source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl", "target": "repair_c01_update_500", "warmstart": "league_v1_cycle_1_exploiter", "win_rate": 0.53925}
{"artifact_dir": "/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05", "elapsed_seconds": 15.667906627990305, "event": "diminishing_returns_complete", "report": "docs/reports/diminishing-returns-2026-07-05.md"}
docs/reports/diminishing-returns-2026-07-05.md
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# Diminishing Returns Diagnostic - 2026-07-05
신규 학습 없이 기존 체크포인트와 평가 롤아웃만 사용했다. 모든 duplicate 평가는 셔플 뱅크 seed 20260704, 2,000쌍(4000 games) 기준이다.
Raw artifacts: `/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05`
## Checkpoints
| Order | Name | Config | Checkpoint |
| ---: | --- | --- | --- |
| 0 | `ladder_v2_gate3` | `configs/jax_ppo/ladder-v2-expert.yaml` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/ladder-v2/2026-07-05_013223_jax-ppo-ladder-v2-expert/latest` |
| 1 | `league_v1_update_250` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/main_ppo_config.json` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/snapshots/cycle_01_update_000250` |
| 2 | `league_v1_update_500` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/main_ppo_config.json` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/snapshots/cycle_01_update_000500` |
| 3 | `repair_c01_update_500` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/gates-1-2/2026-07-05_094238_gates-1-2/gate2c/league/2026-07-05_125709_jax-ppo-gates-1-2-repair-c01/main_ppo_config.json` | `/mnt/2tbhdd/coolrl-lost-cities-artifacts/gates-1-2/2026-07-05_094238_gates-1-2/gate2c/league/2026-07-05_125709_jax-ppo-gates-1-2-repair-c01/snapshots/cycle_01_update_000500` |
## 1. Adjacent Head-to-Head
![Adjacent H2H](diminishing-returns-2026-07-05-h2h.png)
| Later | Earlier | Win rate | Wilson CI | Mean diff | Score CI | Opened colors | Max-step |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
| `league_v1_update_250` | `ladder_v2_gate3` | 0.8015 | [0.7889, 0.8136] | +42.8757 | [+41.3138, +44.4377] | 4.9110 | 0.0000 |
| `league_v1_update_500` | `league_v1_update_250` | 0.5405 | [0.5250, 0.5559] | +5.2097 | [+3.7546, +6.6649] | 4.9415 | 0.0000 |
| `repair_c01_update_500` | `league_v1_update_500` | 0.5323 | [0.5168, 0.5477] | +3.6412 | [+2.2361, +5.0464] | 4.9395 | 0.0000 |
## 2. External Anchor Trajectory
![Expert anchor](diminishing-returns-2026-07-05-expert.png)
| Checkpoint | Win rate | Wilson CI | Mean diff | Score CI | Opened colors | Max-step |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| `ladder_v2_gate3` | 0.8380 | [0.8263, 0.8491] | +43.4560 | [+42.0948, +44.8172] | 4.5592 | 0.0000 |
| `league_v1_update_250` | 0.7438 | [0.7300, 0.7570] | +26.4232 | [+25.1920, +27.6545] | 4.5477 | 0.0000 |
| `league_v1_update_500` | 0.6462 | [0.6313, 0.6609] | +13.9670 | [+12.8797, +15.0543] | 4.8472 | 0.0000 |
| `repair_c01_update_500` | 0.6927 | [0.6783, 0.7069] | +19.6540 | [+18.4924, +20.8156] | 4.7015 | 0.0000 |
Recent expert-anchor slope: `+5.6870` points (league_v1_update_500 -> repair_c01_update_500).
## 3. Exploitability Trajectory
![Exploitability](diminishing-returns-2026-07-05-exploiter.png)
프로토콜이 다른 피탈률은 한 곡선에 섞지 않았다. `Delta`는 같은 프로토콜 안에서만 계산했다.
| Phase | Target | Protocol | Budget | Warm start | Win rate | Delta same protocol | Mean diff | Source |
| --- | --- | --- | --- | --- | ---: | ---: | ---: | --- |
| `ladder_v2` | `ladder_v2_gate3` | `short_random_shaping` | same-budget phase-1 exploiter | `random` | 0.83135 | n/a | +54.7721 | [ladder-v2-2026-07-05-summary.jsonl](ladder-v2-2026-07-05-summary.jsonl) |
| `league_v1` | `league_v1_update_250` | `league_cycle_exploiter` | league v1 cycle exploiter | `random` | 0.50225 | n/a | +0.6818 | [league-v1-2026-07-05-summary.jsonl](league-v1-2026-07-05-summary.jsonl) |
| `league_v1` | `league_v1_update_500` | `league_cycle_exploiter` | same exploiter vs final | `random` | 0.47095 | -0.03130 | -2.6378 | [league-v1-2026-07-05-summary.jsonl](league-v1-2026-07-05-summary.jsonl) |
| `gates_1_2_original` | `league_v1_update_500` | `long_random_shaping` | 1200 updates, shaping anneal | `random` | 0.58700 | n/a | +10.9512 | [gates-1-2-2026-07-05-summary.jsonl](gates-1-2-2026-07-05-summary.jsonl) |
| `gates_1_2_original` | `league_v1_update_500` | `warmstart_gate3_no_shaping` | 900 updates, shaping 0 | `ladder_v2_gate3` | 0.59000 | n/a | +11.3808 | [gates-1-2-2026-07-05-summary.jsonl](gates-1-2-2026-07-05-summary.jsonl) |
| `gates_1_2_original` | `league_v1_update_500` | `replay_exploiter_no_shaping` | 900 updates, shaping 0 | `league_v1_cycle_1_exploiter` | 0.58200 | n/a | +10.4253 | [gates-1-2-2026-07-05-summary.jsonl](gates-1-2-2026-07-05-summary.jsonl) |
| `repair_c01` | `repair_c01_update_500` | `long_random_shaping` | 1200 updates, shaping anneal | `random` | 0.53900 | -0.04800 | +6.2793 | [gates-1-2-2026-07-05-summary.jsonl](gates-1-2-2026-07-05-summary.jsonl) |
| `repair_c01` | `repair_c01_update_500` | `warmstart_gate3_no_shaping` | 900 updates, shaping 0 | `ladder_v2_gate3` | 0.54900 | -0.04100 | +7.0285 | [gates-1-2-2026-07-05-summary.jsonl](gates-1-2-2026-07-05-summary.jsonl) |
| `repair_c01` | `repair_c01_update_500` | `replay_exploiter_no_shaping` | 900 updates, shaping 0 | `league_v1_cycle_1_exploiter` | 0.53925 | -0.04275 | +6.1022 | [gates-1-2-2026-07-05-summary.jsonl](gates-1-2-2026-07-05-summary.jsonl) |
## Judgment
추가 학습 1사이클(~1.5시간 GPU)의 기대 개선은 인접 H2H 최근 이득 +3.64점, expert 앵커 최근 변화 +5.69점, 동일 프로토콜 exploiter 평균 피탈률 감소 0.044에 따라 대략 +3.6~+5.7점 또는 피탈률 -0.044 내외 수준으로 추정되며, 수확체감 구간에 진입했다. 근거는 최근 H2H 이득이 첫 리그 전이보다 작고, expert 앵커 성능이 v1 update 250 이후 하락/회복을 반복하며, 보수 후 exploiter 최악값이 0.549로 게이트 바로 아래에 머문다는 세 측정의 일치다. 단, repair가 expert 앵커를 유의하게 회복시킨 점은 별도 긍정 신호다.
## Notes
- H2H와 expert 앵커 평가는 이번 작업에서 새로 실행했다.
- Exploiter 표는 지금까지 생성된 리포트/JSONL의 기존 측정값만 재정리했다.
- 이 작업에서는 신규 학습, 봇 수정, 체크포인트 수정이 없었다.
scripts/diminishing_returns.py
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from __future__ import annotations
import json
import time
from dataclasses import dataclass
from itertools import pairwise
from pathlib import Path
from typing import Any
import matplotlib.pyplot as plt
from lost_cities_jax.ppo import (
evaluate_checkpoint_match,
evaluate_checkpoint_vs_static,
load_config,
)
DATE = "2026-07-05"
ARTIFACT_DIR = Path("/mnt/2tbhdd/coolrl-lost-cities-artifacts/diminishing-returns/2026-07-05")
REPORT_PATH = Path(f"docs/reports/diminishing-returns-{DATE}.md")
SUMMARY_PATH = Path(f"docs/reports/diminishing-returns-{DATE}-summary.jsonl")
GAMES_PER_SEAT = 2000
TOTAL_DUPLICATE_GAMES = GAMES_PER_SEAT * 2
@dataclass(frozen=True)
class CheckpointSpec:
name: str
label: str
config: str
checkpoint: str
stage_index: int
CHECKPOINTS = [
CheckpointSpec(
name="ladder_v2_gate3",
label="ladder v2 gate3",
config="configs/jax_ppo/ladder-v2-expert.yaml",
checkpoint="/mnt/2tbhdd/coolrl-lost-cities-artifacts/ladder-v2/2026-07-05_013223_jax-ppo-ladder-v2-expert/latest",
stage_index=0,
),
CheckpointSpec(
name="league_v1_update_250",
label="league v1 update 250",
config="/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/main_ppo_config.json",
checkpoint="/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/snapshots/cycle_01_update_000250",
stage_index=1,
),
CheckpointSpec(
name="league_v1_update_500",
label="league v1 update 500",
config="/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/main_ppo_config.json",
checkpoint="/mnt/2tbhdd/coolrl-lost-cities-artifacts/league/2026-07-05_052325_jax-ppo-league-v1/snapshots/cycle_01_update_000500",
stage_index=2,
),
CheckpointSpec(
name="repair_c01_update_500",
label="repair c01 update 500",
config="/mnt/2tbhdd/coolrl-lost-cities-artifacts/gates-1-2/2026-07-05_094238_gates-1-2/gate2c/league/2026-07-05_125709_jax-ppo-gates-1-2-repair-c01/main_ppo_config.json",
checkpoint="/mnt/2tbhdd/coolrl-lost-cities-artifacts/gates-1-2/2026-07-05_094238_gates-1-2/gate2c/league/2026-07-05_125709_jax-ppo-gates-1-2-repair-c01/snapshots/cycle_01_update_000500",
stage_index=3,
),
]
EXPLOITER_ROWS = [
{
"phase": "ladder_v2",
"target": "ladder_v2_gate3",
"protocol": "short_random_shaping",
"budget": "same-budget phase-1 exploiter",
"warmstart": "random",
"win_rate": 0.83135,
"mean_score_diff": 54.7721,
"source": "docs/reports/ladder-v2-2026-07-05-summary.jsonl",
},
{
"phase": "league_v1",
"target": "league_v1_update_250",
"protocol": "league_cycle_exploiter",
"budget": "league v1 cycle exploiter",
"warmstart": "random",
"win_rate": 0.50225,
"mean_score_diff": 0.6818,
"source": "docs/reports/league-v1-2026-07-05-summary.jsonl",
},
{
"phase": "league_v1",
"target": "league_v1_update_500",
"protocol": "league_cycle_exploiter",
"budget": "same exploiter vs final",
"warmstart": "random",
"win_rate": 0.47095,
"mean_score_diff": -2.63775,
"source": "docs/reports/league-v1-2026-07-05-summary.jsonl",
},
{
"phase": "gates_1_2_original",
"target": "league_v1_update_500",
"protocol": "long_random_shaping",
"budget": "1200 updates, shaping anneal",
"warmstart": "random",
"win_rate": 0.587,
"mean_score_diff": 10.95125,
"source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl",
},
{
"phase": "gates_1_2_original",
"target": "league_v1_update_500",
"protocol": "warmstart_gate3_no_shaping",
"budget": "900 updates, shaping 0",
"warmstart": "ladder_v2_gate3",
"win_rate": 0.590,
"mean_score_diff": 11.38075,
"source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl",
},
{
"phase": "gates_1_2_original",
"target": "league_v1_update_500",
"protocol": "replay_exploiter_no_shaping",
"budget": "900 updates, shaping 0",
"warmstart": "league_v1_cycle_1_exploiter",
"win_rate": 0.582,
"mean_score_diff": 10.42525,
"source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl",
},
{
"phase": "repair_c01",
"target": "repair_c01_update_500",
"protocol": "long_random_shaping",
"budget": "1200 updates, shaping anneal",
"warmstart": "random",
"win_rate": 0.539,
"mean_score_diff": 6.27925,
"source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl",
},
{
"phase": "repair_c01",
"target": "repair_c01_update_500",
"protocol": "warmstart_gate3_no_shaping",
"budget": "900 updates, shaping 0",
"warmstart": "ladder_v2_gate3",
"win_rate": 0.549,
"mean_score_diff": 7.0285,
"source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl",
},
{
"phase": "repair_c01",
"target": "repair_c01_update_500",
"protocol": "replay_exploiter_no_shaping",
"budget": "900 updates, shaping 0",
"warmstart": "league_v1_cycle_1_exploiter",
"win_rate": 0.53925,
"mean_score_diff": 6.10225,
"source": "docs/reports/gates-1-2-2026-07-05-summary.jsonl",
},
]
def main() -> None:
started = time.perf_counter()
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
REPORT_PATH.parent.mkdir(parents=True, exist_ok=True)
h2h_rows = run_h2h()
expert_rows = run_expert_anchor()
exploiter_rows = add_exploiter_deltas(EXPLOITER_ROWS)
plot_paths = write_plots(h2h_rows, expert_rows, exploiter_rows)
report = build_report(h2h_rows, expert_rows, exploiter_rows, plot_paths)
REPORT_PATH.write_text(report, encoding="utf-8")
write_summary(h2h_rows, expert_rows, exploiter_rows, time.perf_counter() - started)
def run_h2h() -> list[dict[str, Any]]:
rows = []
for previous, current in pairwise(CHECKPOINTS):
output = ARTIFACT_DIR / f"h2h_{current.name}_vs_{previous.name}.json"
result = evaluate_checkpoint_match(
load_config(current.config),
current.checkpoint,
load_config(previous.config),
previous.checkpoint,
games=GAMES_PER_SEAT,
duplicate=True,
output=output,
)
rows.append(
{
"event": "h2h_adjacent",
"previous": previous.name,
"current": current.name,
"previous_label": previous.label,
"current_label": current.label,
"json": str(output),
**summarize_match(result),
}
)
return rows
def run_expert_anchor() -> list[dict[str, Any]]:
rows = []
for spec in CHECKPOINTS:
output = ARTIFACT_DIR / f"expert_anchor_{spec.name}.json"
result = evaluate_checkpoint_vs_static(
load_config(spec.config),
spec.checkpoint,
"heuristic_expert",
games=GAMES_PER_SEAT,
duplicate=True,
output=output,
)
rows.append(
{
"event": "expert_anchor",
"checkpoint": spec.name,
"label": spec.label,
"stage_index": spec.stage_index,
"json": str(output),
**summarize_match(result),
}
)
return rows
def summarize_match(result: dict[str, Any]) -> dict[str, Any]:
return {
"games": result["games"],
"win_rate": result["win_rate"],
"wilson_low": result["wilson_low"],
"wilson_high": result["wilson_high"],
"mean_score_diff": result["mean_score_diff"],
"score_diff_ci95_low": result["score_diff_ci95_low"],
"score_diff_ci95_high": result["score_diff_ci95_high"],
"score_diff_std": result["score_diff_std"],
"mean_game_length": result["mean_game_length"],
"max_steps_rate": result["max_steps_rate"],
"opened_colors_per_game": result["opened_colors_per_game"],
"play_action_rate": result["play_action_rate"],
"positive_expeditions_per_game": result["positive_expeditions_per_game"],
}
def add_exploiter_deltas(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
enriched = [dict(row) for row in rows]
by_protocol: dict[str, list[dict[str, Any]]] = {}
for row in enriched:
by_protocol.setdefault(row["protocol"], []).append(row)
for protocol_rows in by_protocol.values():
previous = None
for row in protocol_rows:
row["delta_from_previous_same_protocol"] = (
None if previous is None else row["win_rate"] - previous["win_rate"]
)
previous = row
return enriched
def write_plots(
h2h_rows: list[dict[str, Any]],
expert_rows: list[dict[str, Any]],
exploiter_rows: list[dict[str, Any]],
) -> dict[str, str]:
paths = {
"h2h": REPORT_PATH.parent / f"diminishing-returns-{DATE}-h2h.png",
"expert": REPORT_PATH.parent / f"diminishing-returns-{DATE}-expert.png",
"exploiter": REPORT_PATH.parent / f"diminishing-returns-{DATE}-exploiter.png",
}
plt.figure(figsize=(7.5, 4.2))
x = list(range(len(h2h_rows)))
means = [row["mean_score_diff"] for row in h2h_rows]
lows = [row["score_diff_ci95_low"] for row in h2h_rows]
highs = [row["score_diff_ci95_high"] for row in h2h_rows]
plt.errorbar(
x,
means,
yerr=[
[m - lo for m, lo in zip(means, lows, strict=True)],
[hi - m for m, hi in zip(means, highs, strict=True)],
],
fmt="o-",
capsize=4,
)
plt.axhline(0, color="black", linewidth=1)
plt.xticks(x, [row["current"] for row in h2h_rows], rotation=20, ha="right")
plt.ylabel("Mean score diff vs previous")
plt.title("Adjacent checkpoint gains")
plt.tight_layout()
plt.savefig(paths["h2h"], dpi=160)
plt.close()
plt.figure(figsize=(7.5, 4.2))
x = [row["stage_index"] for row in expert_rows]
means = [row["mean_score_diff"] for row in expert_rows]
lows = [row["score_diff_ci95_low"] for row in expert_rows]
highs = [row["score_diff_ci95_high"] for row in expert_rows]
plt.errorbar(
x,
means,
yerr=[
[m - lo for m, lo in zip(means, lows, strict=True)],
[hi - m for m, hi in zip(means, highs, strict=True)],
],
fmt="o-",
capsize=4,
)
plt.axhline(0, color="black", linewidth=1)
plt.xticks(x, [row["checkpoint"] for row in expert_rows], rotation=20, ha="right")
plt.ylabel("Mean score diff vs heuristic_expert")
plt.title("External anchor trajectory")
plt.tight_layout()
plt.savefig(paths["expert"], dpi=160)
plt.close()
plt.figure(figsize=(8.2, 4.4))
protocols = sorted({row["protocol"] for row in exploiter_rows})
for protocol in protocols:
series = [row for row in exploiter_rows if row["protocol"] == protocol]
plt.plot(
[row["target"] for row in series],
[row["win_rate"] for row in series],
"o-",
label=protocol,
)
plt.axhline(0.55, color="black", linewidth=1, linestyle="--", label="0.55 gate")
plt.xticks(rotation=20, ha="right")
plt.ylabel("Exploiter win rate")
plt.title("Exploitability by protocol")
plt.legend(fontsize=8)
plt.tight_layout()
plt.savefig(paths["exploiter"], dpi=160)
plt.close()
return {key: str(path) for key, path in paths.items()}
def build_report(
h2h_rows: list[dict[str, Any]],
expert_rows: list[dict[str, Any]],
exploiter_rows: list[dict[str, Any]],
plot_paths: dict[str, str],
) -> str:
expert_recent = expert_rows[-1]["mean_score_diff"] - expert_rows[-2]["mean_score_diff"]
expert_recent_ci_crosses_zero = (
expert_rows[-1]["score_diff_ci95_low"] <= expert_rows[-2]["score_diff_ci95_high"]
and expert_rows[-2]["score_diff_ci95_low"] <= expert_rows[-1]["score_diff_ci95_high"]
)
same_protocol_repairs = [
row
for row in exploiter_rows
if row["phase"] == "repair_c01" and row["delta_from_previous_same_protocol"] is not None
]
repair_deltas = [row["delta_from_previous_same_protocol"] for row in same_protocol_repairs]
mean_repair_drop = -sum(repair_deltas) / len(repair_deltas)
expected = estimate_expected_improvement(h2h_rows, expert_recent, mean_repair_drop)
judgment = (
f"추가 학습 1사이클(~1.5시간 GPU)의 기대 개선은 인접 H2H 최근 이득 "
f"{h2h_rows[-1]['mean_score_diff']:+.2f}점, expert 앵커 최근 변화 "
f"{expert_recent:+.2f}점, 동일 프로토콜 exploiter 평균 피탈률 감소 "
f"{mean_repair_drop:.3f}에 따라 대략 {expected} 수준으로 추정되며, "
f"수확체감 구간에 진입했다. 근거는 최근 H2H 이득이 첫 리그 전이보다 작고, "
f"expert 앵커 성능이 v1 update 250 이후 하락/회복을 반복하며, 보수 후 "
f"exploiter 최악값이 0.549로 게이트 바로 아래에 머문다는 세 측정의 일치다."
)
if not expert_recent_ci_crosses_zero:
judgment += " 단, repair가 expert 앵커를 유의하게 회복시킨 점은 별도 긍정 신호다."
lines = [
f"# Diminishing Returns Diagnostic - {DATE}",
"",
"신규 학습 없이 기존 체크포인트와 평가 롤아웃만 사용했다. 모든 duplicate 평가는 "
f"셔플 뱅크 seed 20260704, 2,000쌍({TOTAL_DUPLICATE_GAMES} games) 기준이다.",
"",
f"Raw artifacts: `{ARTIFACT_DIR}`",
"",
"## Checkpoints",
"",
"| Order | Name | Config | Checkpoint |",
"| ---: | --- | --- | --- |",
]
for spec in CHECKPOINTS:
lines.append(
f"| {spec.stage_index} | `{spec.name}` | `{spec.config}` | `{spec.checkpoint}` |"
)
lines.extend(
[
"",
"## 1. Adjacent Head-to-Head",
"",
f"![Adjacent H2H]({Path(plot_paths['h2h']).name})",
"",
"| Later | Earlier | Win rate | Wilson CI | Mean diff | Score CI | Opened colors | Max-step |",
"| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |",
]
)
for row in h2h_rows:
lines.append(match_table_row(row, row["current"], row["previous"]))
lines.extend(
[
"",
"## 2. External Anchor Trajectory",
"",
f"![Expert anchor]({Path(plot_paths['expert']).name})",
"",
"| Checkpoint | Win rate | Wilson CI | Mean diff | Score CI | Opened colors | Max-step |",
"| --- | ---: | ---: | ---: | ---: | ---: | ---: |",
]
)
for row in expert_rows:
lines.append(anchor_table_row(row))
lines.extend(
[
"",
f"Recent expert-anchor slope: `{expert_recent:+.4f}` points "
f"({expert_rows[-2]['checkpoint']} -> {expert_rows[-1]['checkpoint']}).",
"",
"## 3. Exploitability Trajectory",
"",
f"![Exploitability]({Path(plot_paths['exploiter']).name})",
"",
"프로토콜이 다른 피탈률은 한 곡선에 섞지 않았다. `Delta`는 같은 프로토콜 안에서만 계산했다.",
"",
"| Phase | Target | Protocol | Budget | Warm start | Win rate | Delta same protocol | Mean diff | Source |",
"| --- | --- | --- | --- | --- | ---: | ---: | ---: | --- |",
]
)
for row in exploiter_rows:
delta = row["delta_from_previous_same_protocol"]
delta_text = "n/a" if delta is None else f"{delta:+.5f}"
source_name = Path(row["source"]).name
lines.append(
f"| `{row['phase']}` | `{row['target']}` | `{row['protocol']}` | "
f"{row['budget']} | `{row['warmstart']}` | {row['win_rate']:.5f} | "
f"{delta_text} | {row['mean_score_diff']:+.4f} | [{source_name}]({source_name}) |"
)
lines.extend(
[
"",
"## Judgment",
"",
judgment,
"",
"## Notes",
"",
"- H2H와 expert 앵커 평가는 이번 작업에서 새로 실행했다.",
"- Exploiter 표는 지금까지 생성된 리포트/JSONL의 기존 측정값만 재정리했다.",
"- 이 작업에서는 신규 학습, 봇 수정, 체크포인트 수정이 없었다.",
]
)
return "\n".join(lines) + "\n"
def match_table_row(row: dict[str, Any], left: str, right: str) -> str:
return (
f"| `{left}` | `{right}` | {row['win_rate']:.4f} | "
f"[{row['wilson_low']:.4f}, {row['wilson_high']:.4f}] | "
f"{row['mean_score_diff']:+.4f} | "
f"[{row['score_diff_ci95_low']:+.4f}, {row['score_diff_ci95_high']:+.4f}] | "
f"{row['opened_colors_per_game']:.4f} | {row['max_steps_rate']:.4f} |"
)
def anchor_table_row(row: dict[str, Any]) -> str:
return (
f"| `{row['checkpoint']}` | {row['win_rate']:.4f} | "
f"[{row['wilson_low']:.4f}, {row['wilson_high']:.4f}] | "
f"{row['mean_score_diff']:+.4f} | "
f"[{row['score_diff_ci95_low']:+.4f}, {row['score_diff_ci95_high']:+.4f}] | "
f"{row['opened_colors_per_game']:.4f} | {row['max_steps_rate']:.4f} |"
)
def estimate_expected_improvement(
h2h_rows: list[dict[str, Any]],
expert_recent: float,
mean_repair_drop: float,
) -> str:
latest_h2h = h2h_rows[-1]["mean_score_diff"]
previous_h2h = h2h_rows[-2]["mean_score_diff"]
conservative_h2h = max(0.0, min(latest_h2h, previous_h2h))
expert_component = max(0.0, expert_recent)
return (
f"+{conservative_h2h:.1f}~+{max(conservative_h2h, expert_component):.1f}점 "
f"또는 피탈률 -{mean_repair_drop:.3f} 내외"
)
def write_summary(
h2h_rows: list[dict[str, Any]],
expert_rows: list[dict[str, Any]],
exploiter_rows: list[dict[str, Any]],
elapsed_seconds: float,
) -> None:
rows = []
rows.extend(h2h_rows)
rows.extend(expert_rows)
rows.extend({"event": "exploiter_trajectory", **row} for row in exploiter_rows)
rows.append(
{
"event": "diminishing_returns_complete",
"elapsed_seconds": elapsed_seconds,
"artifact_dir": str(ARTIFACT_DIR),
"report": str(REPORT_PATH),
}
)
SUMMARY_PATH.write_text(
"\n".join(json.dumps(row, ensure_ascii=False, sort_keys=True) for row in rows) + "\n",
encoding="utf-8",
)
if __name__ == "__main__":
main()