Method Drift›LLM reasoning / chain-of-thought
Superseded baseline#92 of 772 most-superseded
Outcome Reward Models
LLM reasoning / chain-of-thought
superseded — cited as a baseline and beaten by newer methods
2 papers critique it · 0 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites Outcome Reward Models as a baseline.
“Traditional outcome-only verifiers (Outcome Reward Models) are limited, evaluating only the final answer and often missing intermediate errors that compromise the reasoning trajectory Wang2024.”
— Training Vision-Language Process Reward Models for Test-Time Scaling in Multimodal Reasoning: Key Insights and Lessons Learned“most applications of RLVR to date focus on outcome-level verification, where the model receives a scalar reward only if the final answer is correct. While such outcome rewards improve overall performance, they provide little guidance on the internal reasoning process itself. As a result, a model may reach a correct conclusion through unsound, inconsistent, or opaque reasoning traces, limiting interpretability and trustworthiness.”
— Beyond Outcome Verification: Verifiable Process Reward Models for Structured Reasoning
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- Verifiable Process Reward Models (VPRMs)Beyond Outcome Verification: Verifiable Process Reward Models for Structured ReasoningJan 23, 2026
- perception-focused supervisionTraining Vision-Language Process Reward Models for Test-Time Scaling in Multimodal Reasoning: Key Insights and Lessons LearnedSep 27, 2025