R0041/2026-03-28/Q003/SRC04

Research	R0041 — Enterprise Sycophancy
Run	2026-03-28
Query	Q003
Search	S03
Result	S03-R01
Source	SRC04

DeepSeek-R1 paper — seminal RLVR implementation demonstrating emergent reasoning.

Source

Field	Value
Title	DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Publisher	arXiv / Nature
Author(s)	DeepSeek Team
Date	2025-01
URL	https://arxiv.org/abs/2501.12948
Type	Research paper

Summary

Dimension	Rating
Reliability	High
Relevance	High
Bias: Missing data	Low risk
Bias: Measurement	Low risk
Bias: Selective reporting	Some concerns
Bias: Randomization	N/A — not an RCT
Bias: Protocol deviation	N/A — not an RCT
Bias: COI/Funding	Some concerns

Rationale

Dimension	Rationale
Reliability	Seminal RLVR paper, published in Nature. Widely cited and replicated.
Relevance	The foundational implementation of RLVR for reasoning, directly relevant to understanding RLVR's capabilities and limitations.
Bias flags	DeepSeek has commercial interest in demonstrating their approach's effectiveness. The paper acknowledges limitations (narrow domain focus).

Evidence Extracts

Evidence ID	Summary
SRC04-E01	DeepSeek-R1 RLVR implementation: rule-based rewards, math/code domains, acknowledged limitations