We are recruiting! 😊
We are again recruiting @bold_lab_ai - please share the love 🙏. We are looking for:
1) Postdocs (my.corehr.com/pls/uoxrecruit…) -- deadline 9th of Oct at noon
2) research assistants (my.corehr.com/pls/uoxrecruit…) -- deadline 9th of Oct at noon
3) Strategic Partnership Project Manager (my.corehr.com/pls/uoxrecruit…) -- deadline 14th of Oct at noon
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British Open-ended Learning and Discovery Lab retweeted
I recently had a had an hour long conversation with @SAIRfoundation - if you are curious about AI Beyond Scaling, @bold_lab_ai , the journey here, and were we are headed this is for you!
piped.video/watch?v=6BZs0bVR…
PS:
If you'd like to join this journey, the applications for the first ever batch of BOLD Fellows will close at noon UK Time today: jobs.ac.uk/job/DSR362/bold-f…
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British Open-ended Learning and Discovery Lab retweeted
That's what @bold_lab_ai is trying to solve in AI academia in the UK.
🔥Does academia stifle creativity? This new perspective argues that academia is currently structured to select against creativity, intellectual risk-taking and bold ideas. Young scientists – who may be best positioned for new ideas – are particularly incentivized to play it safe.
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British Open-ended Learning and Discovery Lab retweeted
We recently hosted our first @bold_lab_ai Festival in Oxford @UniofOxford, brining together 350 researchers, innovators, industry partners, VCs and policymakers. Our annual BOLD Festivals are core to our efforts of strengthening the open-source AI ecosystem in the UK and in Europe:
eng.ox.ac.uk/news/british-op…
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BOLD 🤝 @AIatMeta
🔥🔥 New work co-led by members of BOLD on dramatically improving the efficiency of AI Research Agents. AI Research Preference Models (RPMs) improve both the ceiling and efficiency of research agents! Check it out 👇
AI research agents can generate hundreds of ideas in seconds, but evaluating each can take days of GPU time. When compute is limited, which idea deserves execution?
We introduce AI Research Preference Models (RPMs) to assess ideas & focus compute on the most promising paths 🧵👇
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🦋 Join BOLD as a fellow, and work on exceptionally impactful research! Applications open now 👇
TL;DR: This is one of the most important and exciting opportunities in AI on the planet - please read on.
The British Open-ended Learning & Discovery Lab is creating the perfect place for paradigm breaking AI research in the name of open-source and open-science. We have agency, we funding, we have unprecedented amounts of compute*, but WE NEED YOU!
..and we have created the dream job for you: The BOLD Fellow. This job combines a fast-moving, high agency, collaborative environment with full academic freedom and a salary that pays the bills.
Apply by noon UK time on the 15th of September for this once in a lifetime opportunity to shape the history of our field and of our planet:
my.corehr.com/pls/uoxrecruit…
*by academic standards
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👊 Some BOLD representation at @RL_Conference this week! Make sure to chat to our BOLDies about their work if you're attending!
🧠 Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments, led by @mcbeukman
🌐 Fully Offline Reinforcement Learning, led by Mattie Fellows and @ClarisseWibault
☄️ Hierarchical Behaviour Spaces, led by @mitrma
❓ When Do We Need LLMs? A Diagnostic for Language-Driven Bandits, led by @uljadb99
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British Open-ended Learning and Discovery Lab retweeted
1/ We are happy to announce the largest known dataset of expert trajectories for physics-based tasks, containing over 11M unique levels in Kinetix.
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Come help us shape the future of AI research at BOLD!
BOLD is hiring 🚀
Right now we are looking for two brilliant individuals to help us build the machine that invents the future. If you are up for a challenge and thrive in a fast moving, collaborative, mission driven environment, this is for you..!
Links and more info👇
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We hope you’ve enjoyed a sneak peek of work from BOLD and our collaborators at #ICML2026! See below for a full summary of where you can find us this week:
▶️ (Poster) Procedural Generation of Algorithm Discovery Tasks in Machine Learning, Hall A #1803, Tuesday 10:30 - 12:15, led by @AlexDGoldie
▶️ (Poster) h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning, Hall A #2704, led by Alesia Ivanova @sumeetrm
▶️ (Poster) Goal-Conditioned Agents that Learn Everything All at Once, Hall A #310, Tuesday 14:00 - 15:45, led by @mitrma
▶️ (Poster) Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Creative Writing, Hall A #2605, Wednesday 14:30 - 14:15, led by Tejas Krishnan @sumeetrm
▶️ (Poster) Evolution Strategies at the Hyperscale, Hall A #3712, Thursday 10:30 - 12:15, led by @bidiptas13 @JuanDuquevan Mattie Fellows
▶️ (Poster) Dreaming in Code for Curriculum Learning in Open-Ended Worlds, Hall A #213, Wednesday 17:00 - 18:45, led by @k_mitsides
▶️ (Poster) Evolution Strategies at the Hyperscale, Hall A #3712, Thursday 10:30 - 12:15, led by @bidiptas13 @JuanDuquevan Mattie Fellows
▶️ (Poster) The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind, Hall A #3504, Thursday 14:30 - 16:15, led by @_andreilupu
▶️ (Poster) LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning, Hall A #1705, Thursday 14:30 - 16:15, led by @sumeetrm @DanielNichols10 @CharlieLondon02 Peggy Li Fabio Pizzati
▶️ (Talk) Superhuman Scientific Discovery, RLxF Worskhop, Friday 15:30 - 16:00, by @robertarail
▶️ (Panel) RLxF Worskhop, Friday 16:00 - 17:00, by @robertarail
▶️ (Workshop) Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs, FoGen Workshop, Friday, led by @jakobhartmann99 James Harvey Jhonathan Navott
▶️ (Workshop) Elicitation Format Drives Divergent LLM Geopolitical Forecasts, AI Forecasting Workshop, Saturday, led by @hariharansuhas @michalbravansky
▶️ (Workshop) EGGROLL-IPO: Pluralistic Alignment via Decentralised Post-Training with Population Preferences, Pluralistic Alignment Workshop, Saturday, led by @alfie_lamerton
▶️ (Workshop Spotlight) Abstraction for Offline Goal-Conditioned Reinforcement Learning, DEMO Workshop, Saturday, led by @ClarisseWibault
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Slight erratum… While we’re sure you all wish EGGROLL would be presented twice, one of these time slots was meant to read:
▶️ (✨✨Spotlight✨✨) Recurrent Structural Policy Gradient for Partially Observable Mean Field Games, Hall A #123, Wednesday 14:30-15:45, led by @ClarisseWibault
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Links (in order):
DiscoGen: arxiv.org/pdf/2603.17863
h1: arxiv.org/pdf/2510.07312
LEO: arxiv.org/pdf/2605.23551
Rubric: openreview.net/pdf?id=LShWfv…
RSPG: arxiv.org/pdf/2602.20141
EGGROLL: arxiv.org/pdf/2511.16652
DiCode: arxiv.org/pdf/2602.08194
Decrypto: arxiv.org/pdf/2506.20664
LongCOT: arxiv.org/pdf/2604.14140
Amortising: openreview.net/pdf?id=Yxu0uQ…
Elicitation: openreview.net/pdf?id=G3F96W…
EGGROLL-IPO: openreview.net/pdf?id=5P7ihy…
ARL: arxiv.org/pdf/2605.22711
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Another #ICML2026 BOLD paper, led by @bidiptas13, Mattie Fellows and @JuanDuquevan. We introduce EGGROLL, a novel general-purpose machine learning algorithm that provides a hundredfold increase in training speed over naïve evolution strategies.
EGGROLL practically eliminates the barrier between inference and training, allowing us to easily fine-tune LLMs for reasoning or train new architectures from scratch.
Check the paper at: arxiv.org/pdf/2511.16652
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🚀 Three (yes, three!) papers on long-horizon reasoning and RL for non-verifiable domains from @sumeetrm, @CharlieLondon02, and collaborators at ICML 2026. h1 (Spotlight) trains models to reason over longer horizons using curriculum RL over composed short-horizon data. This allows models to generalize to harder tasks and improves performance even at high pass@k.
LongCoT isolates and benchmarks long-horizon CoT capabilities, pushing models to reason well over tens and hundreds of thousands of tokens in an output. Rubric Curriculum RL uses the generation-verification gap in non-verifiable domains to allow models to self-improve on tasks like creative writing and health advicing.
h1: arxiv.org/pdf/2510.07312
LongCoT: arxiv.org/abs/2604.14140
RcRL: openreview.net/pdf?id=LShWfv…
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🧠 Our next #ICML2026 paper is a BOLD new benchmark, based on the game Decrypto, for LLM theory of mind led by @_andreilupu !
Taking inspiration from cognitive science, this work introduces a new multi-agent environment for LLMs and exposes systematic failures of frontier models when reasoning about the knowledge and beliefs of other agents.
Check out the project page at: sites.google.com/view/decryp…
And the paper: arxiv.org/pdf/2506.20664
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❓Our next BOLD paper at #ICML2026 is led by @HarryMayne5 and @Justinkangs. A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior
When LLMs explain their decisions, can we trust those explanations? We present a new metric based on whether explanations help predict a model’s behaviour on similar inputs. We find self-explanations encode value information about model decision making!
Give the paper a read: arxiv.org/pdf/2602.02639
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🔦 Another #ICML2026 paper from BOLD, and this time it's a SPOTLIGHT led by @ClarisseWibault. In this work, we introduce Recurrent Structural Policy Gradient for Partially Observable Mean-field Games with Common Noise. Our algorithm learns more realistic history-dependent behaviour, while also leveraging known structure to benefit from faster convergence than model-free RL methods!
Check out the project page at: clarisse-wibault.github.io/r…, which includes links to our Mean-Field Game library, as well as a Google Colab example implementation!
And the paper at: arxiv.org/pdf/2602.20141
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🪩 Next #ICML2026 paper: DiscoGen, led by @AlexDGoldie. DiscoGen is a procedural generator of algorithm discovery problems for AI research agents, supporting the creation of over 100 billion diverse tasks! Tasks vary across many axes, such as their field of machine learning or datasets they use. They can even support different evaluations, such as the time an algorithm takes to train (speedrunning ⚡️), its energy usage (efficiency 🌱) or its performance 💪!
Check out the paper at: arxiv.org/pdf/2603.17863
Give the code a look: github.com/AlexGoldie/discog…
Or install the DiscoGen package for your research: pip install discogen
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🛌💭Our next ICML paper is Dreaming in Code, led by @k_mitsides. In this work, LLMs are used as architects of experience: writing worlds in code that scaffold an RL agent towards long-horizon skills it could not discover alone!
Check out the project page at: konstantinosmitsides.github.…
And the paper: arxiv.org/pdf/2602.08194
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