docs: rai simbench docs update

docs/simulation_and_benchmarking/rai_bench.md

@@ -73,16 +73,14 @@ score = (correctly_placed_now - correctly_placed_initially) / initially_incorrec
 
 You can find predefined scene configs in `rai_bench/manipulation_o3de/predefined/configs/`.
 
-Predefined scenarios can be imported like:
+Predefined scenarios can be imported like, chosing tasks by difficulty:
 
 ```python
 from rai_bench.manipulation_o3de import get_scenarios
 
 get_scenarios(levels=["easy", "medium"])
 ```
 
-Choose which task you want by selecting the difficulty, from trivial to very hard scenarios.
-
 ## Tool Calling Agent Benchmark
 
 Evaluates agent performance independently from any simulation, based only on tool calls that the agent makes. To make it independent from simulations, this benchmark introduces tool mocks which can be adjusted for different tasks. This makes the benchmark more universal and a lot faster.
@@ -106,6 +104,7 @@ The `Validator` class can combine single or multiple subtasks to create a single
 
 -   OrderedCallsValidator - requires a strict order of subtasks. The next subtask will be validated only when the previous one was completed. Validator passes when all subtasks pass.
 -   NotOrderedCallsValidator - doesn't enforce order of subtasks. Every subtask will be validated against every tool call. Validator passes when all subtasks pass.
+-   OneFromManyValidator - passes when any one of the given subtasks passes.
 
 ### Task