arxiv arXiv cs.AI · 15h ago · src: 3d ago · research

PLVR uses symbolic backpropagation to learn verifiable reasoning programs

from English

Researchers introduce PLVR (Program Learning with Verifiable Rewards), a post-training method that learns explicit reasoning programs composed of deterministic and neural primitives rather than updating model weights. The approach utilizes symbolic backpropagation, where credit assignment is performed via type inference over primitive signatures instead of estimation.

On LiveCodeBench v6 and Tau2Bench, 30B base models with PLVR outperform reinforcement learning by 27.8 points on average at matched budget.
The method also surpasses frontier models an order of magnitude larger by 13.6 points.
A single primitive library serves both benchmarks, requiring only 100 examples for new tasks without additional finetuning data.
Experiments identify the backward pass as the source of advantage over uniform sampling.

The authors release a symbolic backpropagation library and conformance checker to allow application to other primitive libraries.

Importance 2/3 arXiv cs.AI Research paper Training methods

Benchmarks

Benchmark	Model	Score
LiveCodeBench	30B base models with PLVR	27.8%
τ²-bench	30B base models with PLVR	27.8%

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