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Life sciences · Preprint
arXiv · September 8, 2026
Raises a question worth testing. It does not answer one.
This is a preprint introducing SUN, a reinforcement learning framework that jointly scores goal selection by novelty and reachability. The work presents theoretical proofs (recovery of count-based bonuses, hitting probability bounds, rejection of unreachable goals) and reports consistent outperformance against state-of-the-art methods across multiple benchmark environments. However, it remains unreviewed and addresses algorithmic rather than clinical science.
Preprint. Intervention: SUN (Successor-to-Novelty): a reachability-aware goal-selection framework integrating novelty and reachability signals, compatible with any off-policy RL algorithm; includes adaptive goal-selection strategy and lightweight pseudocount meth…. Compared with: State-of-the-art goal-conditioned RL methods.
SUN recovers count-based bonuses in the limit (theoretical result) SUN bounds short-horizon hitting probabilities (theoretical result) SUN provably rejects unreachable goals (theoretical result)
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a preprint describing a novel algorithmic framework with theoretical properties and empirical benchmarks, but lacks peer review and addresses a methodological rather than clinical question in machine learning.
As stated by the source record.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state coverage) have shown promising results, but none scores a goal by novelty and reachability jointly: the two signals are traded off by hand, applied in sequence, or one is neglected outright. In this paper, we introduce a reachability-aware goal-selection framework that explicitly integrates these two aspects, and that can be seamlessly incorporated into any off-policy RL algorithm. To this aim, we propose SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable. We prove that SUN recovers count-based bonuses in the limit, bounds short-horizon hitting probabilities, and provably rejects unreachable goals. We further present an adaptive goal-selection strategy that leverages these properties, and an accurate yet lightweight pseudocount to avoid the overhead of classic methods. We back up all our claims with thorough benchmarks: SUN consistently outperforms state-of-the-art methods in standard and novel environments with unreachable or hard-to-reach states, irreversible transitions, obstacles, mazes, and unbounded spaces.
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