HybridRL
One of the training methods for HybridAgent. Through reinforcement learning, it optimizes the model's policy for switching between text mode and latent-space mode.
Timeline (last 90 days)
The reward signals in the HybridRL stage include answer correctness, output format compliance, and whether tool calls succeed.
The HybridRL training stage is responsible for teaching the model when to think silently and when to put pen to paper and output.
The README recommends training HybridRL with 32 GPUs.
HybridAgent adopts a two-stage post-training pipeline: HybridSFT (hybrid supervised fine-tuning) and HybridRL (hybrid reinforcement learning).
HybridRL uses reinforcement learning to teach the model a mode-switching policy, and the reward signal typically comes from a combination of task completion quality and efficiency metrics, enabling the model to find a balance between accuracy and cost.
All Facts (5)
The reward signals in the HybridRL stage include answer correctness, output format compliance, and whether tool calls succeed.
50%UnverifiedThe HybridRL training stage is responsible for teaching the model when to think silently and when to put pen to paper and output.
50%UnverifiedThe README recommends training HybridRL with 32 GPUs.
50%UnverifiedHybridAgent adopts a two-stage post-training pipeline: HybridSFT (hybrid supervised fine-tuning) and HybridRL (hybrid reinforcement learning).
50%UnverifiedHybridRL uses reinforcement learning to teach the model a mode-switching policy, and the reward signal typically comes from a combination of task completion quality and efficiency metrics, enabling the model to find a balance between accuracy and cost.
50%