Guided-RLVR: Guided Exploration in Reinforcement Learning with Verifiable Rewards
RLVR lifts LLM reasoning, but exploration collapses late in training; guided exploration sustains it. Directly relevant to how reasoning models get trained.
The explainer · 3-minute read · 268 words
The claim
A small controller stops reinforcement learning from narrowing onto one style of reasoning.
The problem
Reinforcement learning with verifiable rewards lifts reasoning scores in math and code.
But its rollouts often collapse onto a narrow set of trajectories.
A narrow explored space limits what the model can learn.
How it works
The method adds a lightweight controller to standard RLVR.
The controller injects meaningful steering tokens into the sampling distribution.
Each token nudges the model toward a different reasoning perspective.
The idea builds on findings that thinking tokens anchor reasoning paths.
Training otherwise stays the same as in plain RLVR.
Figures
What changes
Earlier RLVR work bought reward signal at the cost of exploration.
Guided-RLVR keeps the verifiable rewards and widens exploration as well.
On math and code benchmarks it scores higher with more diverse trajectories.
Limits
The evidence comes from benchmark experiments, not production workloads.
The controller is lightweight, but its steering tokens still need careful design.
How the gains hold at very long horizons remains open.
Source · arXiv wire