GPAI Ledger › Phi-4 Reasoning (Microsoft) › Capture 13 Sep 2026

Phi-4 Reasoning — capture 20260913T124411Z

Verify: sha256sum 6c59a8cab7f9fa27ce9be09aa2c13c523faa863f8fca8a723bce4cdef029cfc2.md must equal the hash above (the filename IS the expected hash); ots verify 6c59a8cab7f9fa27ce9be09aa2c13c523faa863f8fca8a723bce4cdef029cfc2.md.20260913T124411Z.ots -f 6c59a8cab7f9fa27ce9be09aa2c13c523faa863f8fca8a723bce4cdef029cfc2.md (opentimestamps.org) proves the bytes existed no later than the attestation time — an upper bound on the capture time; the fetch time above is the archive's own record (a freshly captured proof reports 'pending' here: the calendars anchor within hours, but this archive only upgrades the stored proof to its anchor on a later run, so expect a day or two). ots verify needs a local Bitcoin Core node (a pruned one is fine); without one, ots info on the proof prints the attesting block height and merkle path to check on any block explorer.

Extracted text

Machine-extracted text (layout may be lost; the authoritative content is the stored file above).

# Data Summary for microsoft_Phi-4-reasoning

## 1. General information

**1.0.1 Version of the Summary:** 1.0

**1.0.2 Last update:** 24-Nov-2025

## 1.1 Model Developer Identification

**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080

## 1.2 Model Identification

**1.2.1 Versioned model name(s):** Phi-4-reasoning

**1.2.2 Model release date:** 30-Apr-2025

## 1.3 Overall training data size and characteristics

### 1.3.1 Size of dataset and characteristics

**1.3.1.A Text training data size:** 1 billion to 1 trillion tokens

**1.3.1.B Text training data content:** Prompts sourced from publicly available websites, existing datasets, and licensed collections, augmented with synthetically generated problems; responses generated using o3-mini including chain-of-thought traces; includes STEM, coding, logical puzzles, and safety/Responsible AI alignment data

**1.3.1.C Image training data size:** Not applicable. Images are not part of the training data

**1.3.1.D Image training data content:** Not applicable

**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data

**1.3.1.F Audio training data content:** Not applicable

**1.3.1.G Video training data size:** Not applicable. Video data is not part of the training data

**1.3.1.H Video training data content:** Not applicable

**1.3.1.I Other training data size:** Not applicable

**1.3.1.J Other training data content:** Not applicable

**1.3.2 Latest date of data acquisition/collection for model training:** 31-Mar-2025

**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No

**1.3.4 Date the training dataset was first used to train the model:** 01-Jan-2025

**1.3.5 Rationale or purpose of data selection:** Datasets were curated to emphasize complex multi-step reasoning and verifiable solutions across STEM, coding, and safety, selecting prompts at the boundary of base model capabilities. Synthetic problems and teacher-generated reasoning traces were used to distill structured chain-of-thought and promote concise, checkable answers, supporting robust reasoning performance and generalization to broader tasks

## 2. List of data sources

### 2.1 Publicly available datasets

**2.1.1 Have you used publicly available datasets to train the model?** Yes

## 2.2 Private non-publicly available datasets obtained from third parties

### 2.2.1 Datasets commercially licensed by rights holders or their representatives

**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** Yes

### 2.2.2 Private datasets obtained from other third-parties

**2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)

## 2.3 Personal Information

**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information

## 2.4 Synthetic data

**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes

## 3. Data processing aspects

### 3.1 Respect of reservation of rights from text and data mining exception or limitation

**3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent

## 3.2 Other information

**3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities

**3.2.2 Was the dataset cleaned or modified before model training?** Yes