mark49152
Premium Member
There seem to be so many different LL and LS options and so much effort has been invested in devising and learning these, with new proposals emerging on almost a weekly basis. Yet, relatively little seems to have been invested in improving F2L, which is the longest and least efficient aspect of a CFOP solve. I have been wondering why that is the case, and what the potential is for improving the efficiency of F2L.
Cross is typically close to optimal and takes on average ~1.5 moves per piece solved. This is with no solved pieces to preserve, so maximum freedom to turn the cube without breaking anything.
At first I thought that steps that have to preserve more solved pieces would be less move-efficient, but that seems not to be the case (at least not until only 3-4 pieces remain). For example, optimal 1LLL has to preserve 12 solved pieces, but still takes on average ~1.6 moves per piece to solve the remaining 8 pieces. Running Cube Explorer to find a few optimal solutions for solving F2L pairs (solving 8 pieces, preserving 4 in the cross) typically found solutions in the 13-14 move range, so again ~1.7 moves per piece. Both of those are surprisingly close to cross efficiency.
What does seem to impact efficiency is the number of pieces solved in the step. Using Cube Explorer on the same examples that gave 13-14 move F2L solutions found optimal solutions of 9-10 moves to solve just the front two pairs, or ~2.4 moves/piece. And according to the wiki, the average optimal solution for one F2L slot = 6.7, or 3.35 moves/piece.
So, steps that solve a small number of pieces are generally less move-efficient. F2L is inefficient because it breaks down into four sub-steps solving two pieces each. A typical non-optimal human F2L solution is 29 moves or ~3.6 moves/piece. This is the primary reason for the high move count of CFOP. OLL/PLL work out at only ~2.8 moves/piece.
(This realization has helped me see the genius in Roux: the first two steps are broken into groups of 5 pieces each, enabling more move-efficient solutions. Second block optimal solutions seem to be in the range 8-10, or ~1.8 moves/piece, so the potential for improving efficiency is much greater than is allowed by optimal CFOP pairs. Not only that, but L6E does not have to be concerned with preserving corners or the other four edges, so can be more move-efficient while remaining intuitive.)
Staying on topic with CFOP, I started looking into multi-slotting to see if it holds potential for improving F2L efficiency. Surprisingly there are very few resources on this, so I assume it is rarely used. (Multi-slotting refers to using the insertion of one pair to help the setup of the next pair.)
I found two main resources. The page at http://f2l.net46.net/multislotting.html gives 8 different inserts and lists 80 cases where setup of the next pair is achieved by one of these insertions. The files at http://absolutemind.pagesperso-orange.fr/ms-angl.html on the other hand list 150 possible cases and give individual solutions for these, but are incomplete. Both of these resources ignore the back two slots. If the back two slots are accounted for, there are 245 cases for each of RUR’ and RU’R’ setup pairs (I suspect there are some mirrors in there but am not smart enough to work them out). Too many to learn in its entirety, but as the first page indicates, there are some simple techniques that can be applied to many cases, perhaps intuitively.
In theory, if multi-slotting could be useful in ~50% of cases, when setting up the last three pairs, and if in each case it eliminates 3 moves from the setup of the next pair without adding additional moves to the insertion, that’s an average saving of 4.5 moves/solve reducing typical F2L pairs to 24.5 moves or ~3.1 moves/piece. So multi-slotting is not a holy grail, just an interesting idea that given the inefficiency of F2L, might be worthy of further development.
Does anyone here use multi-slotting or have thoughts on its potential? Any pointers to other resources or discussions on improving F2L efficiency by merging solutions for pairs would also be appreciated. Otherwise I might just have to learn to block build
.
Cross is typically close to optimal and takes on average ~1.5 moves per piece solved. This is with no solved pieces to preserve, so maximum freedom to turn the cube without breaking anything.
At first I thought that steps that have to preserve more solved pieces would be less move-efficient, but that seems not to be the case (at least not until only 3-4 pieces remain). For example, optimal 1LLL has to preserve 12 solved pieces, but still takes on average ~1.6 moves per piece to solve the remaining 8 pieces. Running Cube Explorer to find a few optimal solutions for solving F2L pairs (solving 8 pieces, preserving 4 in the cross) typically found solutions in the 13-14 move range, so again ~1.7 moves per piece. Both of those are surprisingly close to cross efficiency.
What does seem to impact efficiency is the number of pieces solved in the step. Using Cube Explorer on the same examples that gave 13-14 move F2L solutions found optimal solutions of 9-10 moves to solve just the front two pairs, or ~2.4 moves/piece. And according to the wiki, the average optimal solution for one F2L slot = 6.7, or 3.35 moves/piece.
So, steps that solve a small number of pieces are generally less move-efficient. F2L is inefficient because it breaks down into four sub-steps solving two pieces each. A typical non-optimal human F2L solution is 29 moves or ~3.6 moves/piece. This is the primary reason for the high move count of CFOP. OLL/PLL work out at only ~2.8 moves/piece.
(This realization has helped me see the genius in Roux: the first two steps are broken into groups of 5 pieces each, enabling more move-efficient solutions. Second block optimal solutions seem to be in the range 8-10, or ~1.8 moves/piece, so the potential for improving efficiency is much greater than is allowed by optimal CFOP pairs. Not only that, but L6E does not have to be concerned with preserving corners or the other four edges, so can be more move-efficient while remaining intuitive.)
Staying on topic with CFOP, I started looking into multi-slotting to see if it holds potential for improving F2L efficiency. Surprisingly there are very few resources on this, so I assume it is rarely used. (Multi-slotting refers to using the insertion of one pair to help the setup of the next pair.)
I found two main resources. The page at http://f2l.net46.net/multislotting.html gives 8 different inserts and lists 80 cases where setup of the next pair is achieved by one of these insertions. The files at http://absolutemind.pagesperso-orange.fr/ms-angl.html on the other hand list 150 possible cases and give individual solutions for these, but are incomplete. Both of these resources ignore the back two slots. If the back two slots are accounted for, there are 245 cases for each of RUR’ and RU’R’ setup pairs (I suspect there are some mirrors in there but am not smart enough to work them out). Too many to learn in its entirety, but as the first page indicates, there are some simple techniques that can be applied to many cases, perhaps intuitively.
In theory, if multi-slotting could be useful in ~50% of cases, when setting up the last three pairs, and if in each case it eliminates 3 moves from the setup of the next pair without adding additional moves to the insertion, that’s an average saving of 4.5 moves/solve reducing typical F2L pairs to 24.5 moves or ~3.1 moves/piece. So multi-slotting is not a holy grail, just an interesting idea that given the inefficiency of F2L, might be worthy of further development.
Does anyone here use multi-slotting or have thoughts on its potential? Any pointers to other resources or discussions on improving F2L efficiency by merging solutions for pairs would also be appreciated. Otherwise I might just have to learn to block build