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battam1111/README.md

Yanjun Chen

PhD candidate at The Hong Kong Polytechnic University (joint training with EIT, Ningbo), working on reinforcement learning for LLMs and embodied AI. I make the training environment (reward models, verifiers, curricula) measurable and trainable, judged by what each piece does to the model it trains.

Homepage · Email · Google Scholar · ORCID · Hong Kong

What I build

  • C3: exact per-decision credit for cooperative LLM agents by transcript replay, with a method-agnostic audit of credit quality. [paper]
  • AccuracyParadox-RLHF: reproducible RLHF training and evaluation pipelines and reference reward models. [paper, EMNLP 2024]

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  1. Myco Myco Public

    The living armor an AI agent inhabits: eternal devouring, eternal evolution, eternal amplification.

    Rust 64 6

  2. EIT-EAST-Lab/C3 EIT-EAST-Lab/C3 Public

    Official implementation of the paper "Contextual Counterfactual Credit Assignment for Multi-Agent Reinforcement Learning in LLM Collaboration". (by Yanjun Chen)

    Python 36

  3. AccuracyParadox-RLHF AccuracyParadox-RLHF Public

    [EMNLP 2024 Main] Official implementation of the paper "The Accuracy Paradox in RLHF: When Better Reward Models Don't Yield Better Language Models".

    Python 8