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Achievement: Pull SharkAchievement: Starstruck

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battam1111/README.md

Yanjun Chen

Homepage · Google Scholar · ORCID

Hi 👋, I'm Yanjun. Second-year PhD at Hong Kong PolyU, joint with EIT Ningbo.

In reinforcement learning, models are trainable. The environments that train them are not.
I'm working on closing that gap.

What I'm Building

C3 measures exact credit in cooperative LLM agents. Accuracy Paradox(EMNLP 2024) shows that a better reward model does not always train a better policy.

On the side: OmniSeek, a self-hosted deep-retrieval engine for AI agents, and Myco, persistent memory infrastructure. Both in the MCP ecosystem.

Also into board games 🎲, jazz 🎷, karaoke 🎤, and old-school anime that never quite went away.

Best reached by email.

Pinned Loading

  1. AccuracyParadox-RLHFAccuracyParadox-RLHFPublic

    [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

  2. EIT-EAST-Lab/C3EIT-EAST-Lab/C3Public

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

    Python 36

  3. MycoMycoPublic

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

    Rust 63 7

  4. omniseekomniseekPublic

    Your agent seeks what search can't find. A self-hosted perception MCP server that transcribes speech, reads behind logins, sees images and video frames, crosses languages, and remembers.

    Python 43 5