Modal has developed a novel approach to reinforcement learning (RL) for large language models that utilizes mixed-precision and mixed-accelerator samplers. This method addresses the significant cost of sampling in RL by allowing samplers to operate independently of the trainer, using lower precisions and different hardware like FP8 or NVFP4. The system employs off-policy correction and autoscaling serverless GPUs to maintain training stability and performance, even with asynchronous updates and varying sampler configurations. This enables more cost-effective RL training, demonstrated by training the Qwen3.6-35B-A3B model on coding tasks with up to 256K context. AI
IMPACT Enables more cost-effective training of large language models through optimized sampling strategies.
RANK_REASON The item details a novel technical approach and experimental results for improving reinforcement learning training efficiency, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
- A100
- bfloat16
- FP8
- H200
- Megatron-LM
- Modal
- MXFP4
- NVFP4
- Nvidia B200
- Qwen3.6 35B-A3B
- reinforcement learning
- SGLang
- SWE-bench Pro V2
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