add relative_sizes and token_counts

README.md

@@ -14,60 +14,24 @@ tags:
 
 # Olmix Swarm Datasets
 
-This repository contains proxy run data for data mixing using **[Olmix](https://github.com/allenai/olmix/)**. Each swarm consists of multiple proxy training runs with different domain mixture ratios and their corresponding evaluation metrics across various downstream tasks.
+This repository contains proxy run swarm datasets for data mixing using **[Olmix](https://github.com/allenai/olmix/)**. Each swarm consists of multiple proxy training runs with different domain mixture ratios and their corresponding evaluation metrics across various downstream tasks. The swarm datasets here are based on **30M parameter** proxy models trained on **3B tokens**.
 
 For more details, see our paper: [*Olmix: A Framework for Data Mixing Throughout LM Development*](https://arxiv.org/abs/2602.12237)
 
-## Dataset Overview
+## Swarms
 
-The dataset contains **32 swarms** organized into four main categories:
-
-- **Main Swarm (`dclm_swarm/`)**: The primary DCLM swarm used throughout our paper
-- **Research Question Studies (`study/`)**: Swarms for RQ3 (dense vs sparse, different priors) and RQ6 (constrained optimization)
-- **Mixture Reuse (`mixture_reuse/`)**: Real-world update scenarios and theory validation experiments
-- Each swarm includes `ratios.csv`, `metrics.csv`, and `meta.json` with complete metadata
-
-## Usage
-
-### Primary Use Case: Proposing a Mix using OlmixBase
-
-The primary way to use these datasets is to download these CSVs (`ratios.csv`, `metrics.csv` together) directly and use them with the [Olmix GitHub](https://github.com/allenai/olmix) ([example](https://github.com/allenai/olmix/blob/main/configs/fits/dclm_baseline.yaml)). This is the recommended workflow for data mixture optimization.
-
-
-### Alternative: HuggingFace Datasets Library
-
-You can also load all swarms at once using the HuggingFace datasets library (useful for exploration):
-
-```python
-from datasets import load_dataset
-
-# Load the entire collection
-dataset = load_dataset("allenai/olmix")
-
-# Browse available swarms
-for example in dataset["train"]:
-    print(f"{example['description']} - {example['num_runs']} runs, {example['num_domains']} domains")
-
-# Filter by category
-mixture_reuse_swarms = [ex for ex in dataset["train"] if "mixture_reuse" in ex["category"]]
-print(f"Found {len(mixture_reuse_swarms)} mixture reuse swarms")
-```
-
-## Dataset Structure
+The dataset contains **33 swarms**. Each swarm consists of  `ratios.csv`, `metrics.csv`, and `meta.json`.
 
 ```
 dclm_swarm/                          # Main DCLM swarm (1 swarm)
-│   ├── ratios.csv
-│   ├── metrics.csv
-│   └── meta.json
 ├── study/
-│   ├── rq3/                         # Dense vs sparse comparison (5 swarms)
+│   ├── rq3/                         # Swarm distribution experiments (5 swarms)
 │   │   ├── dclm_dense_swarm/
 │   │   ├── source_level_sparse_swarm/
 │   │   ├── source_level_dense_swarm/
 │   │   ├── dclm_swarm_strong_prior/
 │   │   └── dclm_swarm_weak_prior/
-│   └── rq6/                         # Constrained optimization (1 swarm)
+│   └── rq6/                         # Constrained swarm for enforcing repetition (1 swarm)
 │       └── dclm_constrained_swarm/
 └── mixture_reuse/
     ├── real_world/
@@ -79,9 +43,9 @@ dclm_swarm/                          # Main DCLM swarm (1 swarm)
     │   ├── partial_reuse/           # Partial mixture reuse (6 swarms)
     │   │   ├── update1_add_stack_edu_recompute_software_dev_seed{0,1,2}/
     │   │   └── update5_partition_pdfs_recompute_mix_seed{0,1,2}/
-    │   └── swarm_reuse/             # Swarm reuse (1 swarm)
-    │       └── update4_remove_algebraicstack/
-    └── theory_validation/           # Theory validation (6 swarms)
+    │   ├── swarm_reuse/             # Swarm reuse baseline (1 swarm)
+    │   └── full_recomputation/      # Full recomputation baseline (1 swarm)
+    └── theory_validation/           # Theoretical validation (6 swarms)
         ├── dclm_stackedu/
         ├── dclm_pdfs/
         ├── weak_mix_reuse_gap_dclm_stackedu/
@@ -90,91 +54,73 @@ dclm_swarm/                          # Main DCLM swarm (1 swarm)
         └── intermediate_mix_reuse_gap_dclm_pdfs/
 ```
 
-## Dataset Fields
-
-Each example in the dataset contains:
+### dclm_swarm/ — Main Swarm (1 swarm)
 
-| Field | Type | Description |
-|-------|------|-------------|
-| `swarm_id` | string | Unique swarm identifier |
-| `description` | string | Human-readable swarm description |
-| `category` | string | Category/folder path (e.g., "dclm_swarm", "mixture_reuse/real_world/full_reuse") |
-| `notes` | string | Detailed description of the swarm configuration |
-| `dataset_path` | string | Relative path to the dataset |
-| `ratios` | string | JSON string of domain mixture ratios (pandas DataFrame) |
-| `metrics` | string | JSON string of evaluation metrics (pandas DataFrame) |
-| `num_runs` | int | Number of proxy training runs |
-| `num_domains` | int | Number of domains in the mixture |
-| `num_metrics` | int | Number of evaluation metrics |
+- **DCLM main swarm**: 128 proxy runs over 24 DCLM topics (sparse), used as the primary swarm throughout our paper
 
-## Swarm Categories
+### study/ — Olmix Study (6 swarms)
 
-### Main Swarm (1 swarm)
-- **DCLM main swarm**: 128 proxy runs over 24 DCLM topics (sparse), used throughout our paper
+These swarms correspond to results in **Section 3.3** of the paper.
 
-### RQ3: Dense vs Sparse Comparison (5 swarms)
+**RQ3: Swarm Distribution (5 swarms)**
 - **DCLM dense swarm**: 128 proxy runs over 24 DCLM topics (dense)
 - **Source level sparse swarm**: 64 proxy runs over 7 sources (sparse)
 - **Source level dense swarm**: 64 proxy runs over 7 sources (dense)
-- **DCLM swarm strong prior**: 128 proxy runs with strong Dirichlet prior
-- **DCLM swarm weak prior**: 128 proxy runs with weak Dirichlet prior
+- **DCLM swarm strong prior**: 128 proxy runs over 24 DCLM topics with strong Dirichlet prior
+- **DCLM swarm weak prior**: 128 proxy runs over 24 DCLM topics with weak Dirichlet prior
+
+**RQ6: Constrained Optimization (1 swarm)**
+- **DCLM constrained swarm**: 128 proxy runs that satisfy repetition constraints for R=6T requested tokens and k=4 repetition factor
+
+### mixture_reuse/real_world/ — Real World Development (20 swarms)
 
-### RQ6: Constrained Optimization (1 swarm)
-- **DCLM constrained swarm**: 128 proxy runs with R=6T requested tokens and k=4 repetition factor
+These swarms correspond to the main results in **Section 5.1, Figure 10** of the paper.
 
-### Mixture Reuse: Real World - Full Reuse (12 swarms)
-Experiments where the entire previous mixture is reused as a single virtual domain:
+**Full reuse (12 swarms)**: 
 - **Update 1 (add Stack-Edu)**: 3 seeds, swarms over DCLM (1 virtual domain) and Stack-Edu
 - **Update 2 (add more sources)**: 3 seeds, swarms over DCLM+Stack-Edu (1 virtual domain) and new sources
-- **Update 3 (revise PDFs)**: 3 seeds, swarms over new PDF source and unchanged domains (1 virtual domain)
-- **Update 5 (partition PDFs)**: 3 seeds, swarms over PDF topics and unchanged domains (1 virtual domain)
+- **Update 3 (revise PDFs)**: 3 seeds, swarms over revised PDF source and unchanged domains (1 virtual domain)
+- **Update 5 (partition PDFs)**: 3 seeds, swarms over partitioned PDF topics and unchanged domains (1 virtual domain)
 
-### Mixture Reuse: Real World - Partial Reuse (6 swarms)
-Experiments where only part of the mixture is reused, with affected domains recomputed:
+**Partial reuse (6 swarms)**: 
 - **Update 1 (add Stack-Edu, recompute software development)**: 3 seeds, swarms over DCLM \ software development (1 virtual domain), software development, and Stack-Edu
-- **Update 5 (partition PDFs, recompute mix)**: 3 seeds, swarms over PDF topics and source-level
-
-### Mixture Reuse: Real World - Swarm Reuse (1 swarm)
-- **Update 4 (remove AlgebraicStack)**: Swarm after AlgebraicStack is removed is compatible with final domain set
-
-### Mixture Reuse: Theory Validation (6 swarms)
-Experiments validating mixture reuse theory:
-- **DCLM+StackEdu**: Swarm over DCLM+StackEdu (for full recomputation baseline)
-- **DCLM+PDFs**: Swarm over DCLM+PDFs (for full recomputation baseline)
-- **Weak mix reuse gap DCLM+StackEdu**: Swarm over DCLM (reuse weak mix) and Stack-Edu
-- **Intermediate mix reuse gap DCLM+StackEdu**: Swarm over DCLM (reuse intermediate mix) and Stack-Edu
-- **Weak mix reuse gap DCLM+PDFs**: Swarm over DCLM (reuse weak mix) and PDFs
-- **Intermediate mix reuse gap DCLM+PDFs**: Swarm over DCLM (reuse intermediate mix) and PDFs
-
-## Example: Analyzing Domain Distributions
-
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-
-# Load a swarm
-ratios = pd.read_csv("dclm_swarm/ratios.csv")
-metrics = pd.read_csv("dclm_swarm/metrics.csv")
-
-# Get domain columns (excluding metadata columns)
-domain_cols = [c for c in ratios.columns if c not in ['run', 'name', 'index']]
-metric_cols = [c for c in metrics.columns if c not in ['run', 'name', 'index']]
-
-print(f"Domains: {len(domain_cols)}")
-print(f"Metrics: {len(metric_cols)}")
-
-# Plot distribution of a domain
-plt.figure(figsize=(10, 6))
-plt.hist(ratios[domain_cols[0]], bins=30, alpha=0.7)
-plt.xlabel(f"{domain_cols[0]} Ratio")
-plt.ylabel("Frequency")
-plt.title(f"Distribution of {domain_cols[0]} in DCLM Swarm")
-plt.show()
-
-# Analyze metric correlations
-corr_matrix = metrics[metric_cols].corr()
-print(f"Metric correlation matrix: {corr_matrix.shape}")
-```
+- **Update 5 (partition PDFs, recompute mix)**: 3 seeds, swarms over PDF topics and sources
+
+**Swarm reuse (1 swarm)**: Proxy runs accumulated from all swarms that are compatible with the final 64-domain set.
+
+**Full recomputation (1 swarm)**: 512 proxy runs over the final 64 domains after all 5 updates. Subsampled at various budgets to measure full recomputation's performance as a function of run count.
+
+### mixture_reuse/theory_validation/ — Theoretical Validation (6 swarms)
+
+Experiments validating mixture reuse theory (**Section 5.2**):
+- **DCLM+StackEdu** and **DCLM+PDFs**: Swarms for the full recomputation baseline in two settings
+- **Weak/Intermediate mix reuse gap (DCLM+StackEdu)**: Swarms over DCLM (reusing a weak or intermediate mix) and Stack-Edu
+- **Weak/Intermediate mix reuse gap (DCLM+PDFs)**: Swarms over DCLM (reusing a weak or intermediate mix) and PDFs
+
+## Data Format
+
+### ratios.csv
+
+- `run`, `name`, `index`: run identifier, full run name, and sequential index
+- One column per domain: mixture weight (proportion of training tokens) assigned to that domain. Domain names follow the format `source:topic` (e.g., `dclm:politics`, `s2pdf:science_tech`, `stack-edu:Python`), where the prefix is the data source and the suffix is the topic within that source. Single-part names (e.g., `wikipedia`, `arxiv`) are single-domain sources.
+
+### metrics.csv
+
+- `run`, `name`, `index`: same identifiers as in `ratios.csv`
+- One column per evaluation task: the model's **bits-per-byte (BPB)** score on that task. Lower BPB is better. Tasks span a wide range of benchmarks including MMLU, ARC, coding (HumanEval, MBPP), math (Minerva), and reading comprehension.
+
+### meta.json
+
+Each `meta.json` includes `swarm_id`, `description`, `category`, `notes`, `files`, and two fields used when configuring a fit in the [Olmix](https://github.com/allenai/olmix) repository:
+
+- **`relative_sizes`**: The natural distribution of the domains, used in the KL penalty.
+- **`token_counts`**: The final token count for each domain when constructing the repetition constraint.
+
+## Usage
+
+The primary use of these datasets is as input to the [Olmix GitHub repository](https://github.com/allenai/olmix) for fitting a data mixture model. Download `ratios.csv`, `metrics.csv`, and `meta.json` from the desired swarm folder and point to them in your Olmix config `.yaml` file (see [example config](https://github.com/allenai/olmix/blob/main/configs/fits/dclm_baseline.yaml)).
+
+The dataset is also accessible via the HuggingFace datasets library (`load_dataset("allenai/olmix")`) for exploration purposes.
 
 ## Resources
 

dclm_swarm/meta.json

@@ -6,5 +6,57 @@
   "files": {
     "ratios.csv": "Domain mixture ratios for each proxy run",
     "metrics.csv": "Evaluation metrics for each proxy run"
+  },
+  "relative_sizes": {
+    "adult_content": 0.011426145559378242,
+    "art_and_design": 0.017115279947642753,
+    "crime_and_law": 0.03760251381333424,
+    "education_and_jobs": 0.03819063764260111,
+    "electronics_and_hardware": 0.02252846927808186,
+    "entertainment": 0.09499472729589659,
+    "fashion_and_beauty": 0.009181677850877463,
+    "finance_and_business": 0.06572786800235751,
+    "food_and_dining": 0.020291659593193807,
+    "games": 0.0510904592313224,
+    "health": 0.10085821548021548,
+    "history_and_geography": 0.028007795295185975,
+    "home_and_hobbies": 0.034621890011029464,
+    "industrial": 0.010640504851475604,
+    "literature": 0.05367136256765137,
+    "politics": 0.09838421860766539,
+    "religion": 0.04030199076983852,
+    "science_math_and_technology": 0.06933979163830614,
+    "social_life": 0.04443687756758139,
+    "software": 0.025134154331657502,
+    "software_development": 0.04835767747466825,
+    "sports_and_fitness": 0.03455368961865927,
+    "transportation": 0.02686612455867175,
+    "travel_and_tourism": 0.01667626901270792
+  },
+  "token_counts": {
+    "adult_content": 67760078203,
+    "art_and_design": 70659711995,
+    "crime_and_law": 170130914779,
+    "education_and_jobs": 184690792861,
+    "electronics_and_hardware": 80168541745,
+    "entertainment": 441768061760,
+    "fashion_and_beauty": 37256539512,
+    "finance_and_business": 310313927581,
+    "food_and_dining": 105937299687,
+    "games": 229992491702,
+    "health": 393496227836,
+    "history_and_geography": 161049719459,
+    "home_and_hobbies": 126910777314,
+    "industrial": 43572140450,
+    "literature": 364834344848,
+    "politics": 611198130192,
+    "religion": 277776929208,
+    "science_math_and_technology": 427131054341,
+    "social_life": 218731841124,
+    "software": 108039380021,
+    "software_development": 223384974282,
+    "sports_and_fitness": 196759999355,
+    "transportation": 90793306202,
+    "travel_and_tourism": 57642815530
   }
 }

mixture_reuse/real_world/full_recomputation/meta.json

@@ -0,0 +1,142 @@
+{
+  "swarm_id": "ee28fc9c",
+  "description": "Full recomputation swarm",
+  "category": "mixture_reuse/real_world/full_recomputation",
+  "notes": "512 proxy runs over the final 64 domains after the 5 updates. We subsample from this to determine full recomputation's performance at various budgets",
+  "files": {
+    "ratios.csv": "Domain mixture ratios for each proxy run",
+    "metrics.csv": "Evaluation metrics for each proxy run"
+  },
+  "relative_sizes": {
+    "arxiv": 0.04081632653,
+    "dclm:adult_content": 0.007260008378781617,
+    "dclm:art_and_design": 0.007570683427890269,
+    "dclm:crime_and_law": 0.018228312297343258,
+    "dclm:education_and_jobs": 0.01978829923464438,
+    "dclm:electronics_and_hardware": 0.008589486615330858,
+    "dclm:entertainment": 0.04733229233020377,
+    "dclm:fashion_and_beauty": 0.0039917720905245065,
+    "dclm:finance_and_business": 0.033247920811400884,
+    "dclm:food_and_dining": 0.01135042496619488,
+    "dclm:games": 0.024642052681790323,
+    "dclm:health": 0.04216031012418717,
+    "dclm:history_and_geography": 0.017255327084508186,
+    "dclm:home_and_hobbies": 0.013597583283321514,
+    "dclm:industrial": 0.004668443619570556,
+    "dclm:literature": 0.03908939408990015,
+    "dclm:politics": 0.06548551394749036,
+    "dclm:religion": 0.029761813843007766,
+    "dclm:science_math_and_technology": 0.04576404153537389,
+    "dclm:social_life": 0.023435554405540723,
+    "dclm:software": 0.011575647859110355,
+    "dclm:software_development": 0.023934104386801644,
+    "dclm:sports_and_fitness": 0.021081428501823974,
+    "dclm:transportation": 0.009727854235711368,
+    "dclm:travel_and_tourism": 0.0061760159495475595,
+    "finemath-3plus": 0.1020408163,
+    "pes2o": 0.02040816327,
+    "s2pdf:adult": 2.9834951769534685e-05,
+    "s2pdf:art_design": 0.0007003987723587315,
+    "s2pdf:crime_law": 0.004304497877255197,
+    "s2pdf:education_jobs": 0.013423914190223355,
+    "s2pdf:entertainment": 0.0005735046619634145,
+    "s2pdf:fashion_beauty": 5.652756872639283e-05,
+    "s2pdf:finance_business": 0.005875381689656523,
+    "s2pdf:food_dining": 0.00023137752229735462,
+    "s2pdf:games": 0.0002379401441074673,
+    "s2pdf:health": 0.01188453277839032,
+    "s2pdf:home_hobbies": 0.000365144166266438,
+    "s2pdf:industrial": 0.0027110519108453452,
+    "s2pdf:literature": 0.0032599235022842247,
+    "s2pdf:politics": 0.004002747743592977,
+    "s2pdf:religion": 0.0024809398443353067,
+    "s2pdf:science_tech": 0.04479499276302395,
+    "s2pdf:software": 0.0008012257357437738,
+    "s2pdf:software_dev": 0.00395883882022922,
+    "s2pdf:sports_fitness": 0.0005310539609999115,
+    "s2pdf:transportation": 0.0016066058929999894,
+    "s2pdf:travel": 0.00021038180293057342,
+    "stack-edu:C": 0.0061786719102295004,
+    "stack-edu:CSharp": 0.00940099268591905,
+    "stack-edu:Cpp": 0.01635064398307945,
+    "stack-edu:Go": 0.0018249894127522247,
+    "stack-edu:Java": 0.04090481020788093,
+    "stack-edu:JavaScript": 0.011596372856026719,
+    "stack-edu:Markdown": 0.03773213389237864,
+    "stack-edu:PHP": 0.009649581453994433,
+    "stack-edu:Python": 0.023510988448010806,
+    "stack-edu:Ruby": 0.001809566706914491,
+    "stack-edu:Rust": 0.0018508937755699767,
+    "stack-edu:SQL": 0.009216937068389422,
+    "stack-edu:Shell": 0.0033178202487746814,
+    "stack-edu:Swift": 0.0019703834928440217,
+    "stack-edu:TypeScript": 0.003256642457235611,
+    "wikipedia": 0.02040816327
+  },
+  "token_counts": {
+    "arxiv": 20773846846,
+    "dclm:adult_content": 67760078203,
+    "dclm:art_and_design": 70659711995,
+    "dclm:crime_and_law": 170130914779,
+    "dclm:education_and_jobs": 184690792861,
+    "dclm:electronics_and_hardware": 80168541745,
+    "dclm:entertainment": 441768061760,
+    "dclm:fashion_and_beauty": 37256539512,
+    "dclm:finance_and_business": 310313927581,
+    "dclm:food_and_dining": 105937299687,
+    "dclm:games": 229992491702,
+    "dclm:health": 393496227836,
+    "dclm:history_and_geography": 161049719459,
+    "dclm:home_and_hobbies": 126910777314,
+    "dclm:industrial": 43572140450,
+    "dclm:literature": 364834344848,
+    "dclm:politics": 611198130192,
+    "dclm:religion": 277776929208,
+    "dclm:science_math_and_technology": 427131054341,
+    "dclm:social_life": 218731841124,
+    "dclm:software": 108039380021,
+    "dclm:software_development": 223384974282,
+    "dclm:sports_and_fitness": 196759999355,
+    "dclm:transportation": 90793306202,
+    "dclm:travel_and_tourism": 57642815530,
+    "finemath-3plus": 34057973953,
+    "pes2o": 58552461187,
+    "s2pdf:adult": 303073226,
+    "s2pdf:art_design": 6833185034,
+    "s2pdf:crime_law": 42538674743,
+    "s2pdf:education_jobs": 138127926093,
+    "s2pdf:entertainment": 6069602783,
+    "s2pdf:fashion_beauty": 557917820,
+    "s2pdf:finance_business": 61150044703,
+    "s2pdf:food_dining": 2322982204,
+    "s2pdf:games": 2486095532,
+    "s2pdf:health": 108215933374,
+    "s2pdf:home_hobbies": 3924579643,
+    "s2pdf:industrial": 29389278657,
+    "s2pdf:literature": 31886391090,
+    "s2pdf:politics": 39234116889,
+    "s2pdf:religion": 24729732953,
+    "s2pdf:science_tech": 424245385160,
+    "s2pdf:software": 9146853216,
+    "s2pdf:software_dev": 41841278724,
+    "s2pdf:sports_fitness": 5450913796,
+    "s2pdf:transportation": 17149342957,
+    "s2pdf:travel": 2102425717,
+    "stack-edu:C": 4735074247,
+    "stack-edu:CSharp": 7204525343,
+    "stack-edu:Cpp": 12530445761,
+    "stack-edu:Go": 1398595118,
+    "stack-edu:Java": 31347725888,
+    "stack-edu:JavaScript": 8886972357,
+    "stack-edu:Markdown": 28916320218,
+    "stack-edu:PHP": 7395033318,
+    "stack-edu:Python": 18017832560,
+    "stack-edu:Ruby": 1386775805,
+    "stack-edu:Rust": 1418447132,
+    "stack-edu:SQL": 7063472860,
+    "stack-edu:Shell": 2542637875,
+    "stack-edu:Swift": 1510019025,
+    "stack-edu:TypeScript": 2495753789,
+    "wikipedia": 10067758073
+  }
+}

mixture_reuse/real_world/full_reuse/update1_add_stack_edu_seed0/meta.json

@@ -6,5 +6,41 @@
   "files": {
     "ratios.csv": "Domain mixture ratios for each proxy run",
     "metrics.csv": "Evaluation metrics for each proxy run"
+  },
+  "relative_sizes": {
+    "dclm": 0.75,
+    "stack-edu:C": 0.00865014067293728,
+    "stack-edu:CSharp": 0.01316138975818085,
+    "stack-edu:Cpp": 0.02289090157264869,
+    "stack-edu:Go": 0.0025549851774443176,
+    "stack-edu:Java": 0.05726673428187064,
+    "stack-edu:JavaScript": 0.016234921995839823,
+    "stack-edu:Markdown": 0.05282498744087811,
+    "stack-edu:PHP": 0.013509414033430703,
+    "stack-edu:Python": 0.03291538382194868,
+    "stack-edu:Ruby": 0.002533393389274945,
+    "stack-edu:Rust": 0.002591251285383368,
+    "stack-edu:SQL": 0.0129037118936806,
+    "stack-edu:Shell": 0.004644948347541362,
+    "stack-edu:Swift": 0.0027585368895402655,
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 }

mixture_reuse/real_world/partial_reuse/update5_partition_pdfs_recompute_mix_seed0/meta.json

@@ -6,5 +6,66 @@
   "files": {
     "ratios.csv": "Domain mixture ratios for each proxy run",
     "metrics.csv": "Evaluation metrics for each proxy run"
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 }

mixture_reuse/real_world/partial_reuse/update5_partition_pdfs_recompute_mix_seed1/meta.json

@@ -6,5 +6,66 @@
   "files": {
     "ratios.csv": "Domain mixture ratios for each proxy run",
     "metrics.csv": "Evaluation metrics for each proxy run"
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 }

mixture_reuse/real_world/partial_reuse/update5_partition_pdfs_recompute_mix_seed2/meta.json

@@ -6,5 +6,66 @@
   "files": {
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 }

mixture_reuse/real_world/swarm_reuse/meta.json

@@ -0,0 +1,142 @@
+{
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+    "stack-edu:TypeScript": 2495753789,
+    "wikipedia": 10067758073
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+}

mixture_reuse/real_world/swarm_reuse/update4_remove_algebraicstack/meta.json

@@ -1,10 +0,0 @@
-{
-  "swarm_id": "914e1003",
-  "description": "update 4 (remove AlgebraicStack)",
-  "category": "mixture_reuse/real_world/swarm_reuse",
-  "notes": "Swarm after AlgebraicStack is removed is compatible with final domain set",
-  "files": {
-    "ratios.csv": "Domain mixture ratios for each proxy run",
-    "metrics.csv": "Evaluation metrics for each proxy run"
-  }
-}

mixture_reuse/real_world/swarm_reuse/update4_remove_algebraicstack/ratios.csv

@@ -1,129 +0,0 @@
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mixture_reuse/theory_validation/dclm_pdfs/meta.json

@@ -6,5 +6,99 @@
   "files": {
     "ratios.csv": "Domain mixture ratios for each proxy run",
     "metrics.csv": "Evaluation metrics for each proxy run"
+  },
+  "relative_sizes": {
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+    "dclm:electronics_and_hardware": 0.008135795862851961,
+    "dclm:entertainment": 0.04901752016520712,  
+    "dclm:fashion_and_beauty": 0.006395120045607897,
+    "dclm:finance_and_business": 0.057409129886921914,
+    "dclm:food_and_dining": 0.014787907828141749,
+    "dclm:games": 0.03833590468242429,
+    "dclm:health": 0.058020600126849106,
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+    "dclm:home_and_hobbies": 0.019290399190806332,
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+    "dclm:literature": 0.05603144614048391,
+    "dclm:politics": 0.13008463835532713,
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+    "dclm:software_development": 0.0231475576251483,
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+    "dclm:transportation": 0.01017059816437211,
+    "dclm:travel_and_tourism": 0.007146547075145923,
+    "s2pdf:adult": 7.309563185436484e-05,
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study/rq3/dclm_swarm_weak_prior/meta.json

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study/rq3/source_level_dense_swarm/meta.json

@@ -6,5 +6,23 @@
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study/rq3/source_level_sparse_swarm/meta.json

@@ -6,5 +6,23 @@
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study/rq6/dclm_constrained_swarm/meta.json

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