GCSL with Negative Feedback: Learning from Both Success and Failure
A recent study available on arXiv (2509.03206v2) introduces an enhancement to Goal-Conditioned Supervised Learning (GCSL) by integrating negative feedback, which enables agents to learn from both their successes and failures. This research tackles two significant shortcomings of the initial GCSL model: (1) relying solely on self-generated experiences can intensify existing biases, and (2) the relabeling approach concentrates only on positive results, hindering learning from errors. The newly proposed method, known as 'Goal-Conditioned Supervised Learning with Negative Feedback', seeks to address these challenges by allowing agents to extract policy insights from both successful and unsuccessful outcomes. This work is pertinent for creating long-lasting self-adaptive systems where goals and conditions cannot be entirely predicted during the design phase. The paper has been flagged as a replace-cross type on arXiv, indicating a revision, though the authors and publication date are not mentioned.
Key facts
- arXiv:2509.03206v2 announces a replace-cross type
- The paper proposes an extension to GCSL with negative feedback
- GCSL is a self-supervised alternative to reward-based learning and imitation learning
- Original GCSL has two limitations: bias from self-experiences and no learning from failures
- The new method allows learning from both success and failure
- The research targets long-lived self-adaptive systems
- The paper is available on arXiv
- The authors are not specified in the provided content
Entities
Institutions
- arXiv