diff --git a/cleanrl/ppo_continuous_action_wandb.py b/cleanrl/ppo_continuous_action_wandb.py
index 5730f462..bb4ab8c6 100644
--- a/cleanrl/ppo_continuous_action_wandb.py
+++ b/cleanrl/ppo_continuous_action_wandb.py
@@ -761,6 +761,13 @@ def train(cfg):
             #         risk_loss = train_risk(cfg, risk_model, data, criterion, opt_risk, device)
             #     writer.add_scalar("risk/risk_loss", risk_loss, global_step)
 
+            if cfg.fine_tune_risk == "off" and cfg.use_risk and global_step % cfg.risk_update_period == 0 and global_step >= cfg.start_risk_update:
+                for epoch in tqdm.tqdm(range(cfg.num_risk_epochs)):
+                    risk_data = rb.sample(cfg.risk_batch_size)
+                    risk_loss = risk_update_step(risk_model, risk_data, criterion, opt_risk, device)
+                    writer.add_scalar("risk/risk_loss", risk_loss.item(), global_step)
+
+            '''
             if cfg.fine_tune_risk == "sync" and cfg.use_risk:
                 if cfg.use_risk and buffer_num > cfg.risk_batch_size and cfg.fine_tune_risk:
                     if cfg.finetune_risk_online:
@@ -782,7 +789,7 @@ def train(cfg):
                             data = rb.sample(cfg.risk_batch_size*cfg.num_update_risk)
                         risk_loss = train_risk(cfg, risk_model, data, criterion, opt_risk, device)
                     writer.add_scalar("risk/risk_loss", risk_loss, global_step)              
-
+            '''
             # Only print when at least 1 env is done
             if "final_info" not in infos:
                 continue
@@ -860,7 +867,7 @@ def train(cfg):
                         if cfg.risk_type == "binary":
                             rb.add(f_obs[i], f_next_obs[i], f_actions[i], f_rewards[i], f_dones[i], f_costs[i], (f_risks <= cfg.fear_radius).float(), e_risks.unsqueeze(1))
                         else:
-                            rb.add(f_obs[i], f_next_obs[i], f_actions[i], f_rewards[i], f_dones[i], f_costs[i], f_risks, f_risks)
+                            rb.add(f_obs[i], f_next_obs[i], f_actions[i], f_rewards[i], f_dones[i], f_costs[i], f_risks_quant, f_risks)
 
                     f_obs[i] = None    
                     f_next_obs[i] = None
@@ -994,7 +1001,7 @@ def train(cfg):
     torch.save(agent.state_dict(), os.path.join(wandb.run.dir, "policy.pt"))
     wandb.save("policy.pt")
     if cfg.use_risk:
-        torch.save(model.state_dict(), os.path.join(wandb.run.dir, "risk_model.pt"))
+        torch.save(risk_model.state_dict(), os.path.join(wandb.run.dir, "risk_model.pt"))
         wandb.save("risk_model.pt")
     print(f_ep_len)
     envs.close()