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cs.CL2026
Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs
Ruoxi Zhao, Maziar Raissi
Evaluating LLM coding agents in algorithmic trading is difficult because static benchmarks risk data contamination and numerical backtest outputs require ground truth from actual c…
cs.CL2025
Aligning to What? Limits to RLHF Based Alignment
Logan Barnhart, Reza Akbarian Bafghi, Stephen Becker +1
Reinforcement Learning from Human Feedback (RLHF) is increasingly used to align large language models (LLMs) with human preferences. However, the effectiveness of RLHF in addressin…