Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning

## Episode Summary In this episode, we cover: - **Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning** (Hugging Face Daily) - [Read more](https://huggingface.co/papers/2602.21420) - **MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios** (Hugging Face Daily) - [Read more](https://huggingface.co/papers/2602.22638) - **No One Size Fits All: QueryBandits for Hallucination Mitigation** (Hugging Face Daily) - [Read more](https://huggingface.co/papers/2602.20332) - **Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks** (arXiv) - [Read more](http://arxiv.org/abs/2602.23330v1) - **MediX-R1: Open Ended Medical Reinforcement Learning** (Hugging Face Daily) - [Read more](https://huggingface.co/papers/2602.23363) --- *Sponsored by LimitLess AI*

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Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning

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