-[Ray API Design Tutorial](https://verl.readthedocs.io/en/latest/advance/placement.html)
-[Extend to other RL(HF) algorithms](https://verl.readthedocs.io/en/latest/advance/dpo_extension.html)
-[Add models with the FSDP backend](https://verl.readthedocs.io/en/latest/advance/fsdp_extension.html)
-[Add models with the Megatron-LM backend](https://verl.readthedocs.io/en/latest/advance/megatron_extension.html)
-[Ray API design tutorial](https://verl.readthedocs.io/en/latest/advance/placement.html)
-[Extend to Other RL(HF) algorithms](https://verl.readthedocs.io/en/latest/advance/dpo_extension.html)
-[Add Models with the FSDP Backend](https://verl.readthedocs.io/en/latest/advance/fsdp_extension.html)
-[Add Models with the Megatron-LM Backend](https://verl.readthedocs.io/en/latest/advance/megatron_extension.html)
-[Deployment using Separate GPU Resources](https://github.com/volcengine/verl/tree/main/examples/split_placement)
## Performance Tuning Guide
The performance is essential for on-policy RL algorithm. We write a detailed performance tuning guide to allow people tune the performance. See [here](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html) for more details.
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@@ -116,7 +117,7 @@ verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The
-[Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization](https://arxiv.org/abs/2410.09302)
-[Flaming-hot Initiation with Regular Execution Sampling for Large Language Models](https://arxiv.org/abs/2410.21236)
-[Process Reinforcement Through Implicit Rewards](https://github.com/PRIME-RL/PRIME/)
-[TinyZero](https://github.com/Jiayi-Pan/TinyZero): a reproduction of DeepSeek R1 Zero in countdown and multiplication tasks
-[TinyZero](https://github.com/Jiayi-Pan/TinyZero): a reproduction of DeepSeek R1 Zero recipe for reasoning tasks
-[RAGEN](https://github.com/ZihanWang314/ragen): a general-purpose reasoning agent training framework
We are HIRING! Send us an [email](mailto:haibin.lin@bytedance.com) if you are interested in internship/FTE opportunities in MLSys/LLM reasoning/multimodal alignment.
