Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 38/100, so the next move is to benchmark the weakest pillars before repeating the format.
education training career agenda agenda analysis
Language Models Can Teach Themselves to Program Better - Workshops - Reincarnating Reinforcement Learning
This education training career agenda in Education / Training / Career shows 29 visible agenda rows from iclr.cc and scores 38/100: a thin but inspectable design signal. The clearest public signals sit in Future-of-Work Fit and Learning Transfer; the main limits are Follow Through and Network Design. Visible mechanisms include Participant work, Network design, and Learning transfer. The public record does not show follow-up or tracking, so the score should be read as design intent rather than durable impact. A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as an education training career agenda, with the strongest visible signal in future-of-work fit and learning transfer and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence. This page is an original public-evidence analysis, not a copy of the source agenda or an endorsement of the event. The score places the visible agenda in the thin outcome architecture band. The strongest visible pillars are Future-of-Work Fit, Learning Transfer, and Problem Specificity; the thinnest visible pillars are Follow Through, Network Design, and Personalization. Visible mechanisms include Participant work, Network design, and Learning transfer. The extracted agenda preview includes 29 visible rows. The most common formats are Unknown, Presentation, and Training; the most common inferred purposes are Unknown, Knowledge Transfer, and Skill Building.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?46
Participation Architecture - 46/100. Participant work, contribution, interaction, and alternatives to passive broadcast.
Follow Through?5
Follow Through - 5/100. Owners, dates, commitments, progress checks, and accountability after the room.Missing: Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
Problem Specificity?49
Problem Specificity - 49/100. A clear costly problem, objective, decision, or performance target.
Personalization?34
Personalization - 34/100. Role, path, goal, preparation, or connection tailoring for participants.Missing: Create role-based paths, prepared questions, tailored breakouts, or participant-specific next steps.
Network Design?28
Network Design - 28/100. Structured weak ties, bridge-building, mixers, and relationship persistence.Missing: Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
Learning Transfer?50
Learning Transfer - 50/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.
Evidence Maturity?41
Evidence Maturity - 41/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?51
Future-of-Work Fit - 51/100. Value against time, hybrid reality, accessibility, AI, and meeting load.
Fix the gaps
Field-tested exercises matched to this agenda's weakest pillars, from the exercise library.
Follow Through (5/100)
Exercises that strengthen it: Mental Toughness Workshop • WorkshopBank · 15% Solutions • WorkshopBank · Network Patches
Network Design (28/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Personalization (34/100)
Exercises that strengthen it: Journey Mapping • WorkshopBank · Group Energiser & Perspective-Changing Exercise - Paradigm... · Photo Storytelling
Agenda Preview
The actual agenda we captured. Every block is classified by format and purpose. Open any block to see how we read it; the colored edge shows whether it is participant work, broadcast, logistics, or a showcase.
Room vs wrapper
17 percent of the 29 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventMain ConferenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventWorkshopsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventCommunityUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventSelect Year: (2023)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventTimezone: America/LosAngelesUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
12:00 AMIntroduction VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:10 AMInvited Talk by Avishkar Bhoopchand: Human-Timescale Adaptation in an Open-Ended Task Space Avishkar Bhoopchand VideoPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
12:40 AMRead and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals Yue Wu ⋅ Yewen Fan ⋅ Paul Pu Liang ⋅ Amos Azaria ⋅ Yuanzhi Li ⋅ Tom Mitchell Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:50 AMReduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning Juan Formanek ⋅ Callum R. Tilbury ⋅ Jonathan P Shock ⋅ Kale-ab Tessera ⋅ Arnu Pretorius Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:00 AMLearning to Modulate pre-trained Models in RL Thomas Schmied ⋅ Markus Hofmarcher ⋅ Fabian Paischer ⋅ Razvan Pascanu ⋅ Sepp Hochreiter Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:10 AMDo Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling Kolby Nottingham ⋅ Prithviraj Ammanabrolu ⋅ Alane Suhr ⋅ Yejin Choi ⋅ Hannaneh Hajishirzi ⋅ Sameer Singh ⋅ Roy Fox Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:20 AMTowards A Unified Agent with Foundation Models Norman Di Palo ⋅ Arunkumar Byravan ⋅ Leonard Hasenclever ⋅ Markus Wulfmeier ⋅ Nicolas Heess ⋅ Martin Riedmiller LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:30 AMMerging Decision Transformers: Weight Averaging for Forming Multi-Task Policies Daniel Lawson ⋅ Ahmed Qureshi Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:35 AMDeep Reinforcement Learning with Plasticity Injection Evgenii Nikishin ⋅ Junhyuk Oh ⋅ Georg Ostrovski ⋅ Clare Lyle ⋅ Razvan Pascanu ⋅ Will Dabney ⋅ Andre Barreto Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:40 AMSynthetic Experience Replay Cong Lu ⋅ Philip Ball ⋅ Jack Parker-Holder Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMWhere are we in the search for an Artificial Visual Cortex for Embodied Intelligence? Arjun Majumdar ⋅ Karmesh Yadav ⋅ Sergio Arnaud ⋅ Yecheng Jason Ma ⋅ Claire Chen ⋅ Sneha Silwal ⋅ Aryan Jain ⋅ Vincent-Pierre Berges ⋅ Pieter Abbeel ⋅ Dhruv Batra ⋅ Yixin Lin ⋅ Oleksandr Maksymets ⋅ Aravind Rajeswaran ⋅ Franziska Meier Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:50 AMCal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning Mitsuhiko Nakamoto ⋅ Yuexiang Zhai ⋅ Anikait Singh ⋅ Yi Ma ⋅ Chelsea Finn ⋅ Aviral Kumar ⋅ Sergey Levine Video LinkTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
1:55 AMTGRL: Teacher Guided Reinforcement Learning Algorithm for POMDPs Idan Shenfeld ⋅ Zhang-Wei Hong ⋅ Aviv Tamar ⋅ Pulkit Agrawal Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
2:00 AMCo-Imitation Learning without Expert Demonstration Kun-Peng Ning ⋅ Hu Xu ⋅ Kun Zhu ⋅ Sheng-Jun Huang LinkDemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
2:05 AMPoster SessionPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
4:00 AMLunch BreakMealWellbeing+
Format · LogisticsMealA pacing block. Can carry unstructured networking, not scored as participant work.
Evidence basisMediumRead from source
5:00 AMInvited Talk by Joseph Lim: Skill Reuse in Deep Reinforcement Learning Joseph Lim VideoPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
5:30 AMInvited Talk by Furong Hunag: Adaptable Reinforcement Learning in An Ever-Changing World Furong HuangPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
6:00 AMInvited Talk by Anna Goldie: RL for Chip Design / LLMs Anna Goldie VideoPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
6:30 AMInvited Talk by Sergey Levine: Leveraging Offline Datasets / Foundation Models for Real-World RL Sergey Levine VideoPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
7:00 AMPanel Discussion: Challenges & Open Problems in Reusing Prior Computation Joseph Lim ⋅ Furong Huang ⋅ Marc G Bellemare ⋅ Linxi Fan ⋅ Jeff Clune ⋅ Anna Goldie VideoPanelDeliberation+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisMediumRead from source
7:50 AMClosing RemarksClosing RemarksOrientation+
Format · BroadcastClosing RemarksClosing framing from the stage. Wraps the event, not participatory.
Evidence basisMediumRead from source
all eventPIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav Ram Ramrakhya ⋅ Dhruv Batra ⋅ Erik Wijmans ⋅ Abhishek Das LinkTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventMulti-Environment Pretraining Enables Transfer to Action Limited Datasets David Venuto ⋅ Sherry Yang ⋅ Pieter Abbeel ⋅ Doina Precup ⋅ Igor Mordatch ⋅ Ofir Nachum LinkTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 38/100 and verified 38/100 with no fourth-loop cap.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as an education training career agenda, with the strongest visible signal in future-of-work fit and learning transfer and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence.
Strongest signals: Future-of-Work Fit, Learning Transfer, and Problem Specificity. Weakest signals: Follow Through, Network Design, and Personalization.
How This Agenda Could Improve +
- Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
- Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
- Create role-based paths, prepared questions, tailored breakouts, or participant-specific next steps.
Fastest next move: Add named owners, dated next steps, and a visible continuation path before treating the event as outcome-ready.
Role-Specific Reading +
Sponsor lens
Look beyond exposure. Strong sponsor value would show qualified interaction, problem work, buyer learning, customer evidence, or follow-up. The practical sponsor move is to look for structured introductions, buyer-seller fit, and relationship persistence.
Designer lens
The agenda is useful as a pattern sample from iclr.cc. Redesign attention should go first to the lowest-scoring pillars; in practice, turn the thinnest agenda blocks into participant work.
Executive lens
Treat the visible agenda as an operating plan. The executive move is to require owners, dates, and evidence before treating the event as strategic. If owners, proof, and follow-through are not visible, the public record does not yet prove strategic movement.
Aggregator lens
Treat the source URL as evidence, not decoration. The data-product move is to label the source boundary clearly before ranking the record before ranking or syndicating the record.
What GES Means Here +
The Gathering Effectiveness Score is a strict 0-100 public-evidence reading of the agenda across eight pillars. It rewards visible participant work, follow-through, transfer, network design, and proof mechanisms more than polish, speaker fame, attendance, or satisfaction.
Visible mechanisms: Participant work, Network design, Learning transfer.
Evidence boundary: Scores reflect visible agenda/source evidence and should not be read as proof of causal event impact.
Limitations, Score Caps, and Review Flags +
Limitations
- No visible follow-up, progress monitoring, or longitudinal tracking.
- No baseline measurement is visible.
Score caps
- No fourth-loop score cap applied.
Review flags
- No source-backed follow-up, validation, baseline, tracking, or impact evidence.
Is this proof the event worked? +
No. This is a strict public-evidence reading of the agenda. Proof would require baseline, comparison, follow-up, attribution, and impact evidence beyond the listing.
What should a reader inspect first? +
Start with the source URL, then compare the eight pillar scores against the agenda rows. The biggest opportunities usually sit in follow-through, evidence maturity, and participant work.
Why publish weak records? +
Weak records are part of the map. They show where public agendas still describe sessions and speakers more often than outcomes, commitments, transfer, or proof.
How should I use the rows? +
Read the agenda rows as the visible design trace: formats, purposes, and evidence labels show what the public source made inspectable, not everything that happened in the room. This is a source-grounded interpretation of the public agenda record, not a copy of the source, and not an endorsement of the event.
Embed the verified badge +
This record is in the hand-verified gold set. Copy the snippet below to embed the verified badge on your own site.
<a href="https://unitedwetransform.com/events/evt_2023_iclr_cc_conferences_2023_language_models_can_teach_themselves_to_program_be_8a1800"><img src="https://unitedwetransform.com/badge/ges-verified.svg" alt="GES verified by United We Transform" height="40"></a>Where To Go Next
Compare this agenda against other Education / Training / Career events scored on the same eight pillars.