AsyncTensorRLHF / configs /phase1_sync.yaml
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Initial release of AsyncTensorRLHF: High-Throughput Asynchronous RLHF with Tensor-Native Rewards
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# Phase‑1 synchronous baseline configuration
# Minimal settings to run a single‑node PPO baseline with colocated rollout & trainer.
# Adjust GPU ids, model path, and data paths as needed.
model:
name: "Qwen2.5-0.5B"
path: "${MODEL_PATH}" # set via env var or replace here
training:
epochs: 3
batch_size: 64
learning_rate: 1e-5
optimizer: "adamw"
loss: "ppo"
clip_eps: 0.2
rollout:
backend: "vllm" # or "sglang"
max_new_tokens: 256
temperature: 0.7
top_p: 0.95
replay_buffer:
max_size: 10000
# No disaggregation; trainer and rollout share the same GPUs.