E01¶


Research	R0041 — Enterprise Sycophancy
Run	2026-04-01
Query	Q003
Source	SRC02
Evidence	SRC02-E01
Type	Factual

RLVR applicable domains and reward hacking resistance

URL: https://labelstud.io/blog/reinforcement-learning-from-verifiable-rewards/

Extract¶

RLVR applicable domains include: mathematical problem-solving (GSM8k dataset), code execution and synthesis, instruction-following and formatting, factual accuracy verification, logical consistency checking, and regulatory compliance screening.

Unlike neural reward functions used in RLHF, "verifiable rewards offer several advantages" including deterministic feedback and resistance to reward hacking. Implementation uses PPO to "balance reward maximization with controlled model divergence."

Tiered scoring example: "+1 if all tests pass, -1 if any fail, -0.2 if no valid code."

Relevance to Hypotheses¶

Hypothesis	Relationship	Strength
H1	N/A	Domain list confirms scope but does not address broad applicability
H2	Supports	Domain list confirms RLVR applies to specific verifiable tasks
H3	Contradicts	Reward hacking resistance is relevant to sycophancy in verifiable domains