research
The Best Fork Came Just Before the Model Changed Its Mind
Belief-shift branching placed scarce reinforcement-learning rollouts near value pivots and led the reported math and code comparisons.
Summary
Belief-shift branching placed scarce reinforcement-learning rollouts near value pivots and led the reported math and code comparisons.
Tree-structured reinforcement learning gets step-level credit by forking a reasoning chain and comparing sibling outcomes, but every fork costs samples. This work probes the model’s answer belief at candidate boundaries and branches just before consecutive beliefs diverge most. The placement probe consumed about 1% of step compute on math and under 5% on code. It ranked first against Monte Carlo value curves in eight model-benchmark panels; in training, it improved OLMo-3-7B’s math aggregate by 2.6 points and LiveCodeBench-medium by 6.5 over the strongest reported baseline. Results remain limited to the tested model families and verifiable-reward domains.
Why it matters
Belief-shift branching placed scarce reinforcement-learning rollouts near value pivots and led the reported math and code comparisons.
Limits and context
- Results remain limited to the tested model families and verifiable-reward domains.
Key claims
Belief-shift branching placed scarce reinforcement-learning rollouts near value pivots and led the reported math and code comparisons.
Qualification: Results remain limited to the tested model families and verifiable-reward domains.
Evidence: source-2026-09-11-005
Sources
- arXiv preprint 2609.11061arXiv · primary research
Corrections
No corrections have been recorded for this story.