Update README.md

```bash

# To reproduce our reults, you can run

bash vagen/vagen/examples/release_experiments/gae.sh

bash vagen/vagen/examples/release_experiments/grpo_mask_loss.sh

bash vagen/vagen/examples/release_experiments/grpo.sh

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss_bi_level.sh

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss_turnwise_gae.sh

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss_turnwise_reward_bi_level.sh

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss.sh

bash vagen/vagen/examples/release_experiments/mask_gae.sh

bash vagen/vagen/examples/release_experiments/mask_loss.sh

bash vagen/vagen/examples/release_experiments/gae.sh # rico-gae

bash vagen/vagen/examples/release_experiments/grpo_mask_loss.sh # rico-grpo + loss mask

bash vagen/vagen/examples/release_experiments/grpo.sh # rico-grpo

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss_bi_level.sh # trico - turn reward

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss_turnwise_gae.sh # trico - turn reward - bi-level gae + turn-level gae

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss_turnwise_reward_bi_level.sh # trico

bash vagen/vagen/examples/release_experiments/mask_gae_mask_loss.sh # aico

bash vagen/vagen/examples/release_experiments/mask_gae.sh # aico - loss mask

bash vagen/vagen/examples/release_experiments/mask_loss.sh # aico - gae mask

```

Each run takes ~4 hours to reach 150 steps on 4 H100s. You can decrease testing frequency to speed up training. Training might be unstable due to loss spikes; we recommend restoring from the latest checkpoint when encountering such cases. We will resolve this issue in future work (see roadmap).

## Algorithm Settings

- [ ] Merge to RAGEN for better package mangement

- [ ] Address training stability issues

  - [ ] Lora Support

  - [ ] Implement improved early stopping techniques

  - [ ] Test alternative optimization strategies

  - [ ] Implement improved early stopping and gradient clip strategies

  - [ ] Reward normalization (see RAGEN)

- [ ] Expand evaluation framework to more diverse visual environments

- [ ] Scaling to larger models and applying TRICO to text-only tasks