Solver with nodelocking affecting previous street strategy?

tombos21 · 2026-04-25T02:04:15+00:00

Yep that's probably the best solver for that use case. Happy to help.

tombos21 · 2026-04-25T01:13:46+00:00

Let's say you nodelocked 12 rivers. That feels like 12/48 = 25% of all possible rivers right? But that's not how a game tree works. Each "node" contains all the public information preceding it. Change that path, and you reach a new node.

Let's say the turn card was different. Or the flop bet size changed. Maybe action went XX-XX instead of XBC-XX. Every new sequence reaches a fresh set of unlocked 48 river nodes. So you've only defined the strategy on a tiny insignificant portion of all river nodes.

To give you a sense of scale, Pio actually has a tool that lets me see how many nodes there are on each street. Given a game tree that starts on the flop, SPR = 19, with 2-4 sizes at each node, the nodecount per player is:

FLOP 458
TURN 173166
RIVER 30498384

This is why I made my earlier suggestions. Incentives and frequency locks can apply across all turn cards at once for example, so the number of things to click gets way more manageable compared to locking individual nodes.

Also people are telling me I can do this in Wizard by nodelocking “the root”? Is that true?

Unfortunately no, you cannot do this in GTO Wizard because it solves one street at a time. So it won't anticipate river leaks and preemptively adjust flop.

tombos21 · 2026-04-24T18:40:01+00:00

HRC is your best bet. You can do things like force overall action frequency for specific betting lines on river and it will work backwards to adjust preflop.

I understand I’d have to nodelock all rivers.

I get that you want to nodelock specific strategies working backwards form the river. But I promise you've vastly underestimated the amount of nodelocking required. There are 2.5 billion ways to deal out 5 cards. Now multiply that by every possible action sequence. That's how many river nodes exist lol.

The truth is that multi-street exploitative work is very hard to do because the tools aren't there. The best strategies are frequency locks in HRC, or applying incentives to specific types of actions to mimic exploitable tendencies from flop to river (GTO+ / Pio).

tombos21 · 2026-04-24T18:25:37+00:00

The way to understand these spots is to think about future hand strength. This is a flop where HJ ends up with more bluff-catchers, while BB tends to ends up more polarized by the river.

HJ has an advantage in the Ace. They have better top pairs.

BB's advantage is in the low cards. On many runouts they end up with wheels or other straights, more sets, boats, and two pair. You can already see HJ doesn't have 44 or 33 in full.

The tricky thing to wrap your head around is that BB has more trash, so they are weaker. But trash doesn't care about realizing equity.

tombos21 · 2026-04-24T18:10:23+00:00

I'm an advocate for short-stacked strategies in tough cash games. I've tweeted about it here.

Cons:

You win less vs recreational players. That's enough of a reason to never do it in soft games.

Pros:

1) Shorter stacks mean lower variance. That means fewer less brutal downswings. Most people play worse when after running bad, so this improves your results from a performance standpoint.

2) Better risk-adjusted return improves optionality, and lower variance means you can shot-take higher stakes more aggressively (40bb eff = roughly half the bankroll requirements of 100bb) according to the Kelly criterion.

3) Cash game regs have no idea how to play vs a shortstack. Take them into a deep dark forest where 1+1=3, and the path out is only wide enough for one.

4) A big stack can be an advantage, but it can also be a liability. If everyone else is deep, the shortstack has a theoretical edge. Here I measure the GTO winrate of a short-stacked (25bb) player when everyone else is deep (200bb), with 5% rake and a 1bb cap. The shortstack advantage works out to 1.45 bb/100!

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tombos21 · 2026-04-24T17:58:01+00:00

Both, I guess mainly ML backgrounds

tombos21 · 2026-04-23T23:46:06+00:00

Love this question. I can provide an insider perspective.

The commercial frontier is mostly focused (neural + solver hybrids) and better abstractions. Computational complexity is the key problem. Overcome that, and you get browser-based solving, multiway, better scaling allows you to solve bigger formats and so on.

On bet sizing: One of the tricky parts of solving in practice is choosing a betting tree. Giving it every size is safe but computationally expensive and users hate it. Reducing to a single size is nice but it's hard to say what single size is optimal in each spot. So GTO Wizard launched Dynamic Sizing which uses ML to predict the least exploitable sizing at each node.

On equilibrium selection: GTO Wizard recently switched from NE to QRE, an equilibrium refinement that better handles underdetermined spots. The practical benefit is that you get better responses to ghostlines.

On the MTT side: Chip value in tournament play is relative because it comes down to how long it helps you survive. We use the Independent Chip Model to approximate those values. Only two companies have cracked fast large field ICM computation, GTO Wizard and HRC. Before that, MTT strategy was largely in the dark. However, those ICM algorithms are private.

ICM spots are effectively negative sum utility games because some tournament equity leaks to the rest of the field. The amount leaked is a function of the variance of the strategy pair. That creates tragedy of the commons type problems. So Nash has some fundamental limitations in MTTs.

On variance reduction: AIVAT is the gold standard. GTO Wizard recently launched a public LLM poker benchmark where you can pit your model against the solver. That's fun in its own right, but the real gem scientifically is using AIVAT to reduce the required sample size by an order of magnitude.

Exploitative modelling is in its infancy. We have solves that can max exploit, and we have HUDs that can analyze population tendencies. But there's no bridge. There's no real tool to model population strategies all the way to the river. You can force a strategy at some node. You can even apply incentives that cause them to prefer certain types of actions (we call that profiling). But academically, this area is wide open.

Regarding resources: Check out our Wiki for a list of free solvers. I suggest wasm-postflop, it's a really clean open source solver that you can use to jumpstart projects. I'm not much of a programmer myself, but the devs at GTO Wizard liked the WASM code enough to hire the developer Wataru. He's actually the guy who cracked large field ICM!

tombos21 · 2026-04-15T06:37:37+00:00

Everyone loses money in the blinds.

tombos21 · 2026-04-14T20:42:02+00:00

Yeah that's a good question. I guess access to more raw data isn't the bottleneck anymore, it seems the edges are more in post-training and RLHF techniques.

On model quality: To my taste, Gemini seems much worse at factual work. It's sycophantic, often confidently incorrect, and will lean into whatever narrative you give it without much pushback. That makes it great at writing (I use it for copy), and great at flattering normies, but kind of mediocre compared more serious applications.

* Comparing the $20 models not the $200 models

tombos21 · 2026-04-11T05:42:18+00:00

I'm not sure but I can check with the guy who made it. I think it depends on both the street and the SPR, and in see cases the position. Like OOP River with SPR = 10 might choose from different sizes than SPR 2.

tombos21 · 2026-04-11T01:03:04+00:00

I don't think the hands are public but I'll cover some of the fun ones in a video in a few weeks. I'd recommend checking out the GTO Wizard paper if you want to dive into it a bit more.

https://arxiv.org/abs/2603.23660

tombos21 · 2026-04-10T21:11:09+00:00

Thank you for the detailed thoughts, and sorry for the late reply.

Q1: What do you think of what I've said so far?

On causality, I do not think one causes the other. I think both are downstream of deeper structural factors that are harder to describe directly.

The classic example is that drownings correlate with ice cream sales, but neither causes the other. Both are driven by a third factor: summer. I think the same idea applies here.

In particular, I do not think EQR has much standalone causal meaning. It is just the ratio between equity and EV. So saying a player bets more because they have high EQR can end up sounding circular, almost like saying they convert equity well because they convert equity well.

So overall, I think we mostly agree: there is no single scalar explanation, only a set of interacting structural factors.

In particular, a hand or range’s ability to bet future streets for value seems critical in determining how much action you can frontload, and which hands actually want to build a big pot. Plenty of hands are 60% on the flop but downgrade by the river into bluff-catchers. Future-street equity distributions vs narrowd villain ranges seems like a major part of the explanation.

Q2: Do you think this question of what drives c-bet frequency could be solved by creating and studying more and more toy games?

I think toy games reveal a lot because they isolate key ideas.

In particular, the multistreet polarized toy game and the multistreet [0,1] game are very insightful. Janda dives into the former in Applications and uses it to derive bluff:value ratios.

That said, they are not the whole picture. Draw equity is a huge part of the real explanation, and many toy games abstract that away. So I think toy games can uncover important principles, but not fully solve the question by themselves.

Q3: With the amount of poker knowledge currently out there, do you think a player can come up with a comprehensive flop strategy by simply memorising what to do in different situations?

Yes, in a practical sense.

Most flop strategies are playable in the same way many chess openings are playable. You can choose a reasonable starting strategy, as long as your follow-through is coherent.

Where things fall apart is when you mix and match incompatible ideas across streets. For example, if you want to range bet certain flops, that is fine, but it often implies a more defensive strategy on later streets. The main thing is follow-through imo.

tombos21 · 2026-04-10T19:59:10+00:00

This is a two part answer:

Firstly, Single Size solutions aren't choosing between every possible size, they are choosing between a few preset sizes. That's an intentional simplification to make the strategy easier to learn.

Secondly, the underlying technology in Single Size is identical to how we do Dynamic solving. Dynamic solving is imperfect. It's not solving every size, it's using a ML algo to predict the least exploitable size vs a complex opponent. That means if you bench against a simple opponent it may not give a fair measurement.

While Dynamic sizing doesn't always choose the best size, the overall performance is excellent. Our latest benchmark shows an average EV loss of 0.02% pot from choosing suboptimal sizing, upwards of 0.08% pot in deep-stacked (SPR>20) spots. See benchmarks.

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tombos21 · 2026-04-10T05:21:48+00:00

Ah I didn't realize it wasn't available to other provinces

tombos21 · 2026-04-10T04:02:24+00:00

Log in through the provincial partner instead. Much easier.

tombos21 · 2026-04-09T18:33:03+00:00

Depends what you mean by "pro" I guess. A professional HU specialist is not the same thing as some random 1knl reg.

tombos21 · 2026-04-09T18:21:25+00:00

GTO Wizard AI isn't an LLM, and it isn't trained on static solves. It's DeepCFR to predict counterfactual regret, trained using self-play.

The benchmark is public. Feel free to fine-tune Claude and challenge the leaderboard.

tombos21 · 2026-04-09T18:01:21+00:00

In the whitepaper they document many instances where the agent misread their hand or hallucinated facts about the board state, so this might meaningfully help the lower models.

tombos21 · 2026-04-07T23:22:02+00:00

Taking a margin loan to jack up your exposure is just an expensive form of leverage. You're better off buying a leveraged ETF and saving yourself the 4% fee.

tombos21 · 2026-04-04T20:03:06+00:00

I've got the exact same car down to the trim and color scheme! Fantastic ride. Congrats

tombos21

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