fly51fly (@fly51fly)

TOPReward 논문은 언어모델의 토큰 확률을 로봇 제어를 위한 숨겨진 제로샷 보상으로 활용하는 새로운 접근을 제안합니다. University of Washington과 Amazon 연구진이 제시한 이 방법은 보상 설계 없이 텍스트 기반 확률 정보를 보상 신호로 변환해 로봇 태스크에 적용하는 실험·분석을 담고 있으며 로보틱스에서 제로샷 보상 추출 가능성을 탐구합니다.

https://x.com/fly51fly/status/2028229277972607293

#robotics #rewardlearning #zeroshot #llm

Recent findings show that hippocampal neurons shift their activity backward in time as learning occurs, enabling anticipation of rewards before they happen. Using calcium imaging in mice performing a delayed nonmatching-to-location task, the study tracked the same neurons over weeks and observed a backpropagation of tuning from reward delivery to earlier moments, including the moment of correct touchscreen choice. The results portray the hippocampus as a dynamic predictive map that reorganizes with experience to guide future behavior.

This work is of interest to psychology because it provides concrete neural evidence for predictive coding and memory-based anticipation, illustrating how learning reshapes mental representations to forecast future events. It links memory, prediction, and action in a measurable neural framework.

Article Title: Hippocampal neurons shift their activity backward in time to anticipate rewards

Link to PsyPost Article: ift dot tt/fVBpnxF

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#Hippocampus #PredictiveCoding #NeuralPlasticity #CalciumImaging #RewardLearning

Sumanth (@Sumanth_077)

튜토리얼을 시도해본 후 RULER(Relative Universal LLM-Elicited Rewards)이 에이전트 행동에 자동으로 보상을 할당해 수작업 보상 설계(핸드크래프트 리워드 엔지니어링)를 제거해 준다는 점을 긍정적으로 평가한 코멘트입니다. RULER 기반 자동 보상 할당의 실사용 경험을 공유합니다.

https://x.com/Sumanth_077/status/2016515340776456276

#ruler #rewardlearning #llm #aiagents

#introduction

I work as a researcher at the Fractal Analytics - Cerebral team. #cognitiveneuroscience #emotions #rewardlearning #humanbehavior #decisionmaking are some of the keywords that describe the projects done at Cerebral.