R0041/2026-03-28/Q003/SRC05/E01¶
The emerging modular training stack uses RLVR for reasoning tasks alongside preference optimization for subjective quality, with each method addressing different aspects of model behavior.
URL: https://labelstud.io/blog/reinforcement-learning-from-verifiable-rewards/
Extract¶
Verifiable rewards use "strict, rule-based evaluations rather than learned approximations," delivering binary ground truth signals. Key advantages: direct alignment with ground truth without bias, deterministic evaluation minimizing human judgment, resistance to reward hacking due to binary nature. The emerging industry practice uses a modular stack: SFT for instruction following, preference optimization (DPO/SimPO/KTO) for alignment, and RL with verifiable rewards (GRPO/DAPO) for reasoning tasks. Applicable domains: mathematical correctness, code execution, instruction-following verification, factual accuracy with verifiable answers. The article does not discuss sycophancy.
Relevance to Hypotheses¶
| Hypothesis | Relationship | Strength |
|---|---|---|
| H1 | N/A | Describes modular stack without specifically addressing sycophancy |
| H2 | Contradicts | RLVR's deterministic rewards structurally avoid preference-based bias |
| H3 | Supports | The modular stack explicitly requires preference methods alongside RLVR, confirming RLVR cannot replace them |
Context¶
The modular stack concept is important: it means the industry has already concluded that RLVR and preference methods serve different purposes and neither can replace the other. This directly supports H3.