propose fix some typos

docs/source/tutorial_sources/zero-to-forge/1_RL_and_Forge_Fundamentals.md

@@ -155,7 +155,7 @@ async def conceptual_forge_rl_step(services, step):
 
 **Key difference**: Same RL logic, but each component is now a distributed, fault-tolerant, auto-scaling service.
 
-Did you realise-we are not worrying about any Infra code here! TorchForge Automagically handles the details behind the scenes and you can focus on writing your RL Algorthms!
+Did you realise-we are not worrying about any Infra code here! TorchForge Automagically handles the details behind the scenes and you can focus on writing your RL Algorithms!
 
 
 ## Why This Matters: Traditional ML Infrastructure Fails

src/forge/actors/generator.py

@@ -667,7 +667,7 @@ async def update_weights(
                     loaded = model.load_weights([(name, param)])
                     del param
                     loaded_weights.update(loaded)
-            logger.info(f"[PolicyWorker] updated {len(loaded_weights)} paremeters")
+            logger.info(f"[PolicyWorker] updated {len(loaded_weights)} parameters")
             t.stop()
             return
         # normal update_weights without shared memory prefetching

tests/integration_tests/test_policy_update.py

@@ -191,7 +191,7 @@ async def _setup_and_teardown(request):
 
     if use_dcp_override is not None:
         trainer_cfg["use_dcp"] = use_dcp_override
-        logger.info(f"`trainer.use_dcp` is overriden to {use_dcp_override}")
+        logger.info(f"`trainer.use_dcp` is overridden to {use_dcp_override}")
 
     if cfg.get("provisioner", None) is not None:
         await init_provisioner(