Add Meta-Bandit-LLM, a long-horizon multiturn interative awesome use case of verl (#3756)

[Meta-Bandit-LLM](https://github.com/sanxing-chen/meta-bandit-llm/)
utilizes verl to train on-policy LLM agent with up to 50-turn
interations, with support of async vLLM and LoRA.

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

README.md

@@ -240,6 +240,7 @@ verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The
 - [Table-R1](https://github.com/Table-R1/Table-R1): Table-R1: Inference-Time Scaling for Table Reasoning ![GitHub Repo stars](https://img.shields.io/github/stars/Table-R1/Table-R1)
 - [Revisual-R1](https://github.com/CSfufu/Revisual-R1): Revisual-R1: Advancing Multimodal Reasoning From Optimized Cold Start to Staged Reinforcement Learning ![GitHub Repo stars](https://img.shields.io/github/stars/CSfufu/Revisual-R1)
 - [ARES](https://github.com/shawn0728/ARES): ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping ![GitHub Repo stars](https://img.shields.io/github/stars/shawn0728/ARES)
+- [Meta-Bandit-LLM](https://github.com/sanxing-chen/meta-bandit-llm): Meta-Bandit-LLM: Long-horizon multiturn interactive training for meta-bandit agents ![GitHub Repo stars](https://img.shields.io/github/stars/sanxing-chen/meta-bandit-llm)
 
 and many more awesome work listed in [recipe](recipe/README.md).