Research map / Bandits and online learning / Online convex optimization and games
Counterfactual regret minimization
Improve CFR for extensive-form games with neural approximation and learned algorithm design.
15 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Online convex optimization and games, keyword cluster “game · equilibrium · convergence”
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Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
2 papers in 2023–24 and 2 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 0.9% to 0.7%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Strategy-Based Warm Starting for Regret Minimization in Games (AAAI 2016 · 17 citations)
- 2019 · Deep Counterfactual Regret Minimization (ICML 2019 · 21 citations)
- 2020 · Double Neural Counterfactual Regret Minimization (ICLR 2020 · 15 citations)
- 2021 · Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games (AAAI 2021 · 8 citations)
- 2022 · AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms (AAAI 2022 · 5 citations)
- 2024 · Regret Minimization in Stackelberg Games with Side Information (NeurIPS 2024 · 1 citation)
- 2026 · Faster Game Solving via Asymmetry of Step Sizes (AAAI 2026 · 0 citations)
Earliest papers here
- Strategy-Based Warm Starting for Regret Minimization in Games (AAAI 2016 · 17 citations)
- Deep Counterfactual Regret Minimization (ICML 2019 · 21 citations)
- Stable-Predictive Optimistic Counterfactual Regret Minimization (ICML 2019 · 9 citations)
What has been tried
11 papers here report 104 measured changes (a component added, removed or swapped, compared with a baseline): helped 52%, no clear effect 12%, hurt 27%, mixed 10%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Related approaches
Same keyword cluster
- Last-iterate convergence in games (65 papers)
- Fast no-regret dynamics in games (37 papers)
Elsewhere in the topic
- Online learning with feedback graphs (155 papers)
- Best-of-both-worlds bandit algorithms (125 papers)
- Learning for pricing and auctions (101 papers)
- Constrained and unconstrained OCO algorithms (85 papers)
- Online decision-making with feedback structure (81 papers)
- Online allocation with constraints (77 papers)
- Learning-augmented online algorithms (73 papers)
- Dynamic and adaptive regret (57 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).