When dual-response None is being used, the choice task is coded twice in the design, but only when the respondent says "No, I really wouldn't buy this product". When the respondent says, "Yes, I would buy", then the task is just coded once to capture all the information. So, the number of choice tasks used in the actual coding and estimation depends on the respondent answers.
RLH for CBC-type studies is not so straightforward as a technique for identifying "bad" respondents. That's because a respondent who simplifies to try to complete the survey very rapidly can get a very high RLH. For example, always choosing "None" or "No, I wouldn't buy" in dual-response format will result in a high RLH. Also, a respondent who always picks the lowest priced product in every choice task will also get a high RLH, etc.
However, for any given CBC survey setup, it is interesting to generate, say, 1000 random-responding people and to estimate HB utilities for them. This will give you a distribution of RLH scores that come from truly random-responding cases. A few of those random-responders will get lucky and get a reasonably high RLH. But, most of these respondents will show relatively low RLH and viewing this distribution of RLH scores will give you a good feel for what it means to be a random responder in terms of RLH.
The average RLH for random responders will be different, for each CBC questionnaire of different characteristics: #concepts per task, #tasks, #attributes, #levels, whether dual-response None is used or traditional None. So, this practice of generating 1000 random responders and analyzing via HB needs to be repeated each time you create a CBC survey with different characteristics.