Aletheia: What Makes RLVR For Code Verifiers Tick?
Quick summary
arXiv:2601.12186v4 Announce Type: replace-cross Abstract: Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training. However, their adoption in code generation has lagged behind that of execution feedback due to the prohibitive costs of the full RLVR pipeline. In this work, we ablate three primary choices along the performance-cost trade-off in RLVR: intermediate thinking traces, learning from negative samples, and on-policy training. We introduce Aletheia, a controlled, execution-grounded testbed to facilitate a
Key takeaways
- arXiv:2601.12186v4 Announce Type: replace-cross Abstract: Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training.
- However, their adoption in code generation has lagged behind that of execution feedback due to the prohibitive costs of the full RLVR pipeline.
- In this work, we ablate three primary choices along the performance-cost trade-off in RLVR: intermediate thinking traces, learning from negative samples, and on-policy training.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Aletheia: What Makes RLVR For Code Verifiers Tick?” may reshape data collection, model training, output accountability and market access.
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