Update representative checkpoint model card

README.md

@@ -7,17 +7,21 @@ tags:
 
 # DexPolicy representative checkpoints
 
-This repository contains a representative subset of the object-specific policies reported in the DexPolicy paper. The released pair uses the YCB mustard-bottle relocation task and isolates the paper's exploration-scheduling intervention under GRPO.
+This repository contains a representative subset of the object-specific policies reported in the DexPolicy paper. The released pairs use the YCB mustard-bottle relocation task and isolate the paper's exploration-scheduling intervention under PPO, GRPO, and FPO.
 
 ## Checkpoints
 
-| File | Method | Object | Seed | Continuation |
+| File | Method | Object | Seed | Exploration during training |
 |---|---|---|---:|---|
+| `models/ppo_mustard_baseline_seed0.zip` | PPO baseline | YCB 006 mustard bottle | 0 | Learned policy standard deviation |
+| `models/ppo_mustard_dexpolicy_seed0.zip` | PPO + DexPolicy | YCB 006 mustard bottle | 0 | Piecewise schedule from `0.20` to a final `0.04` plateau |
 | `models/grpo_mustard_baseline_seed0.zip` | GRPO baseline | YCB 006 mustard bottle | 0 | Inherited, learned policy standard deviation |
 | `models/grpo_mustard_dexpolicy_seed0.zip` | GRPO + DexPolicy | YCB 006 mustard bottle | 0 | Scheduled standard deviation, `0.10 -> 0.03` |
+| `models/fpo_mustard_baseline_seed0.pt` | FPO baseline | YCB 006 mustard bottle | 0 | Fixed standard deviation, `0.10` |
+| `models/fpo_mustard_dexpolicy_seed0.pt` | FPO + DexPolicy | YCB 006 mustard bottle | 0 | Linear schedule, `0.20 -> 0.05` |
 
-Both policies continue from matched object-and-seed checkpoints at 507,904 environment steps for another 250,000 steps. They use the same reward, terminal success definition, curriculum stage, and GRPO return-to-go credit module; the exploration schedule is the controlled difference.
+The PPO and FPO pairs train for approximately 5M environment steps with matched settings within each pair. PPO uses an MLP actor mean; FPO uses a conditional-flow actor mean. The GRPO pair continues from matched object-and-seed checkpoints at 507,904 environment steps for another 250,000 steps. It uses the same reward, terminal success definition, curriculum stage, and return-to-go credit module; the exploration schedule is the controlled difference.
 
-The archives follow the Stable-Baselines3 checkpoint layout and include the policy, PyTorch variables, optimizer state, and library-version metadata. These policies are object-specific and are not presented as a single cross-object policy.
+The PPO and GRPO `.zip` archives follow the Stable-Baselines3 checkpoint layout and include the policy, PyTorch variables, optimizer state, and library-version metadata. The FPO `.pt` files are PyTorch training checkpoints for the conditional-flow actor and critic. These policies are object-specific and are not presented as a single cross-object policy.
 
 This is a compact representative release rather than the complete multi-object checkpoint suite.