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Gaotu Techedu Inc.: research and development expense

Research and development expense for Gaotu Techedu Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Gaotu Techedu Inc. financial histories

What this measure means

Research and development costs recognized as expense. Capitalization policies and acquired projects can make this differ from total cash invested in development.

Exact concept: us-gaap:ResearchAndDevelopmentExpense. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2017-01-01 to 2025-12-31. The SEC response was captured on 2026-09-21.

Coverage by original unit

These are separate reported series. A newer period in one unit does not update another unit’s history or establish a currency conversion.

Selected filing history

Research and development expense in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31626,947,000CNY2026-04-2220-F · 0001193125-26-168211
2024-01-012024-12-31648,063,000CNY2026-04-2220-F · 0001193125-26-168211
2023-01-012023-12-31462,043,000CNY2026-04-2220-F · 0001193125-26-168211
2022-01-012022-12-31445,117,000CNY2025-04-2220-F · 0000950170-25-056468
2021-01-012021-12-311,252,877,000CNY2024-04-2520-F · 0000950170-24-047938
2020-01-012020-12-31734,450,000CNY2023-04-1820-F · 0000950170-23-013156
2019-01-012019-12-31212,197,000CNY2022-04-2620-F · 0000950170-22-006141
2018-01-012018-12-3174,050,000CNY2021-04-2620-F · 0001193125-21-130440
2017-01-012017-12-3152,451,000CNY2020-04-0320-F · 0001193125-20-097205
2025-01-012025-12-3189,652,000USD2026-04-2220-F · 0001193125-26-168211
2024-01-012024-12-3188,784,000USD2025-04-2220-F · 0000950170-25-056468
2023-01-012023-12-3165,077,000USD2024-04-2520-F · 0000950170-24-047938
2022-01-012022-12-3164,536,000USD2023-04-1820-F · 0000950170-23-013156
2021-01-012021-12-31196,604,000USD2022-04-2620-F · 0000950170-22-006141
2020-01-012020-12-31112,559,000USD2021-04-2620-F · 0001193125-21-130440
2019-01-012019-12-3130,480,000USD2020-04-0320-F · 0001193125-20-097205

Related financial histories

Inspect the source

Entity
Gaotu Techedu Inc. / CIK 0001768259
Captured
2026-09-21T17:26:39.519Z
SEC response SHA-256
30a2bb024be86c1d52d8f01d5846c8dd21449a3bc47aff1ca06dafe80236846e

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001768259.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))