On this put up, I’ll introduce a reinforcement studying (RL) algorithm based mostly on an “different” paradigm: divide and conquer. In contrast to conventional strategies, this algorithm is not based mostly on temporal distinction (TD) studying (which has scalability challenges), and scales properly to long-horizon duties.
We are able to do Reinforcement Studying (RL) based mostly on divide and conquer, as a substitute of temporal distinction (TD) studying.
Downside setting: off-policy RL
Our drawback setting is off-policy RL. Let’s briefly assessment what this implies.
There are two courses of algorithms in RL: on-policy RL and off-policy RL. On-policy RL means we will solely use recent knowledge collected by the present coverage. In different phrases, we have now to throw away outdated knowledge every time we replace the coverage. Algorithms like PPO and GRPO (and coverage gradient strategies normally) belong to this class.
Off-policy RL means we don’t have this restriction: we will use any type of knowledge, together with outdated expertise, human demonstrations, Web knowledge, and so forth. So off-policy RL is extra normal and versatile than on-policy RL (and naturally tougher!). Q-learning is essentially the most well-known off-policy RL algorithm. In domains the place knowledge assortment is dear (e.g., robotics, dialogue programs, healthcare, and many others.), we regularly haven’t any alternative however to make use of off-policy RL. That’s why it’s such an essential drawback.
As of 2025, I feel we have now moderately good recipes for scaling up on-policy RL (e.g., PPO, GRPO, and their variants). Nonetheless, we nonetheless haven’t discovered a “scalable” off-policy RL algorithm that scales properly to complicated, long-horizon duties. Let me briefly clarify why.
Two paradigms in worth studying: Temporal Distinction (TD) and Monte Carlo (MC)
In off-policy RL, we sometimes practice a price perform utilizing temporal distinction (TD) studying (i.e., Q-learning), with the next Bellman replace rule:
[begin{aligned} Q(s, a) gets r + gamma max_{a’} Q(s’, a’), end{aligned}]
The issue is that this: the error within the subsequent worth $Q(s’, a’)$ propagates to the present worth $Q(s, a)$ via bootstrapping, and these errors accumulate over the whole horizon. That is mainly what makes TD studying battle to scale to long-horizon duties (see this put up when you’re focused on extra particulars).
To mitigate this drawback, folks have blended TD studying with Monte Carlo (MC) returns. For instance, we will do $n$-step TD studying (TD-$n$):
[begin{aligned} Q(s_t, a_t) gets sum_{i=0}^{n-1} gamma^i r_{t+i} + gamma^n max_{a’} Q(s_{t+n}, a’). end{aligned}]
Right here, we use the precise Monte Carlo return (from the dataset) for the primary $n$ steps, after which use the bootstrapped worth for the remainder of the horizon. This manner, we will cut back the variety of Bellman recursions by $n$ instances, so errors accumulate much less. Within the excessive case of $n = infty$, we get better pure Monte Carlo worth studying.
Whereas it is a affordable answer (and infrequently works properly), it’s extremely unsatisfactory. First, it doesn’t basically remedy the error accumulation drawback; it solely reduces the variety of Bellman recursions by a relentless issue ($n$). Second, as $n$ grows, we undergo from excessive variance and suboptimality. So we will’t simply set $n$ to a big worth, and must rigorously tune it for every job.
Is there a basically totally different solution to remedy this drawback?
The “Third” Paradigm: Divide and Conquer
My declare is {that a} third paradigm in worth studying, divide and conquer, could present a really perfect answer to off-policy RL that scales to arbitrarily long-horizon duties.
Divide and conquer reduces the variety of Bellman recursions logarithmically.
The important thing concept of divide and conquer is to divide a trajectory into two equal-length segments, and mix their values to replace the worth of the total trajectory. This manner, we will (in principle) cut back the variety of Bellman recursions logarithmically (not linearly!). Furthermore, it doesn’t require selecting a hyperparameter like $n$, and it doesn’t essentially undergo from excessive variance or suboptimality, not like $n$-step TD studying.
Conceptually, divide and conquer actually has all the great properties we wish in worth studying. So I’ve lengthy been enthusiastic about this high-level concept. The issue was that it wasn’t clear tips on how to really do that in apply… till lately.
A sensible algorithm
In a current work co-led with Aditya, we made significant progress towards realizing and scaling up this concept. Particularly, we had been capable of scale up divide-and-conquer worth studying to extremely complicated duties (so far as I do know, that is the primary such work!) not less than in a single essential class of RL issues, goal-conditioned RL. Objective-conditioned RL goals to study a coverage that may attain any state from every other state. This offers a pure divide-and-conquer construction. Let me clarify this.
The construction is as follows. Let’s first assume that the dynamics is deterministic, and denote the shortest path distance (“temporal distance”) between two states $s$ and $g$ as $d^*(s, g)$. Then, it satisfies the triangle inequality:
[begin{aligned} d^*(s, g) leq d^*(s, w) + d^*(w, g) end{aligned}]
for all $s, g, w in mathcal{S}$.
When it comes to values, we will equivalently translate this triangle inequality to the next “transitive” Bellman replace rule:
[begin{aligned}
V(s, g) gets begin{cases}
gamma^0 & text{if } s = g,
gamma^1 & text{if } (s, g) in mathcal{E},
max_{w in mathcal{S}} V(s, w)V(w, g) & text{otherwise}
end{cases}
end{aligned}]
the place $mathcal{E}$ is the set of edges within the surroundings’s transition graph, and $V$ is the worth perform related to the sparse reward $r(s, g) = 1(s = g)$. Intuitively, which means we will replace the worth of $V(s, g)$ utilizing two “smaller” values: $V(s, w)$ and $V(w, g)$, supplied that $w$ is the optimum “midpoint” (subgoal) on the shortest path. That is precisely the divide-and-conquer worth replace rule that we had been on the lookout for!
The issue
Nonetheless, there’s one drawback right here. The problem is that it’s unclear how to decide on the optimum subgoal $w$ in apply. In tabular settings, we will merely enumerate all states to seek out the optimum $w$ (that is primarily the Floyd-Warshall shortest path algorithm). However in steady environments with giant state areas, we will’t do that. Principally, because of this earlier works have struggled to scale up divide-and-conquer worth studying, regardless that this concept has been round for many years (in truth, it dates again to the very first work in goal-conditioned RL by Kaelbling (1993) – see our paper for an additional dialogue of associated works). The principle contribution of our work is a sensible answer to this problem.
The answer
Right here’s our key concept: we prohibit the search house of $w$ to the states that seem within the dataset, particularly, people who lie between $s$ and $g$ within the dataset trajectory. Additionally, as a substitute of trying to find the optimum $textual content{argmax}_w$, we compute a “tender” $textual content{argmax}$ utilizing expectile regression. Particularly, we reduce the next loss:
[begin{aligned} mathbb{E}left[ell^2_kappa (V(s_i, s_j) – bar{V}(s_i, s_k) bar{V}(s_k, s_j))right], finish{aligned}]
the place $bar{V}$ is the goal worth community, $ell^2_kappa$ is the expectile loss with an expectile $kappa$, and the expectation is taken over all $(s_i, s_k, s_j)$ tuples with $i leq okay leq j$ in a randomly sampled dataset trajectory.
This has two advantages. First, we don’t want to look over the whole state house. Second, we stop worth overestimation from the $max$ operator by as a substitute utilizing the “softer” expectile regression. We name this algorithm Transitive RL (TRL). Try our paper for extra particulars and additional discussions!
Does it work properly?
humanoidmaze
puzzle
To see whether or not our technique scales properly to complicated duties, we immediately evaluated TRL on a few of the most difficult duties in OGBench, a benchmark for offline goal-conditioned RL. We primarily used the toughest variations of humanoidmaze and puzzle duties with giant, 1B-sized datasets. These duties are extremely difficult: they require performing combinatorially complicated abilities throughout as much as 3,000 surroundings steps.
TRL achieves the very best efficiency on extremely difficult, long-horizon duties.
The outcomes are fairly thrilling! In comparison with many sturdy baselines throughout totally different classes (TD, MC, quasimetric studying, and many others.), TRL achieves the very best efficiency on most duties.
TRL matches the very best, individually tuned TD-$n$, while not having to set $boldsymbol{n}$.
That is my favourite plot. We in contrast TRL with $n$-step TD studying with totally different values of $n$, from $1$ (pure TD) to $infty$ (pure MC). The result’s very nice. TRL matches the very best TD-$n$ on all duties, while not having to set $boldsymbol{n}$! That is precisely what we wished from the divide-and-conquer paradigm. By recursively splitting a trajectory into smaller ones, it might probably naturally deal with lengthy horizons, with out having to arbitrarily select the size of trajectory chunks.
The paper has a variety of further experiments, analyses, and ablations. For those who’re , take a look at our paper!
What’s subsequent?
On this put up, I shared some promising outcomes from our new divide-and-conquer worth studying algorithm, Transitive RL. That is just the start of the journey. There are a lot of open questions and thrilling instructions to discover:
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Maybe a very powerful query is tips on how to prolong TRL to common, reward-based RL duties past goal-conditioned RL. Would common RL have the same divide-and-conquer construction that we will exploit? I’m fairly optimistic about this, provided that it’s doable to transform any reward-based RL job to a goal-conditioned one not less than in principle (see web page 40 of this ebook).
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One other essential problem is to cope with stochastic environments. The present model of TRL assumes deterministic dynamics, however many real-world environments are stochastic, primarily because of partial observability. For this, “stochastic” triangle inequalities would possibly present some hints.
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Virtually, I feel there’s nonetheless a variety of room to additional enhance TRL. For instance, we will discover higher methods to decide on subgoal candidates (past those from the identical trajectory), additional cut back hyperparameters, additional stabilize coaching, and simplify the algorithm much more.
Usually, I’m actually excited concerning the potential of the divide-and-conquer paradigm. I nonetheless suppose one of the essential issues in RL (and even in machine studying) is to discover a scalable off-policy RL algorithm. I don’t know what the ultimate answer will appear to be, however I do suppose divide and conquer, or recursive decision-making normally, is likely one of the strongest candidates towards this holy grail (by the way in which, I feel the opposite sturdy contenders are (1) model-based RL and (2) TD studying with some “magic” tips). Certainly, a number of current works in different fields have proven the promise of recursion and divide-and-conquer methods, akin to shortcut fashions, log-linear consideration, and recursive language fashions (and naturally, basic algorithms like quicksort, section bushes, FFT, and so forth). I hope to see extra thrilling progress in scalable off-policy RL within the close to future!
Acknowledgments
I’d prefer to thank Kevin and Sergey for his or her useful suggestions on this put up.
This put up initially appeared on Seohong Park’s weblog.
