谷歌Dream-RSI：让AI 智能体在“梦中”完成自我进化

Dream-RSI:RecursiveSelf-Improvement

throughEvolvingWorlds

TongZheng₁,²,XidongWu¹,ZhengZhang¹,ZhankuiHe³,ChaoyiZhang¹,BenjaminColeman³,RuoqiaoWei¹,DiBai³,HaolinLiu⁴,RuiLiu²,XueWang¹,YueZhuan¹,Wang-ChengKang³,RenkaiXiang¹,HengHuang²,

XinwuCheng¹andYunsongGuo¹

¹Google,²UniversityofMaryland,CollegePark,³GoogleDeepmind,4UniversityofVirginia

arXiv:2609.14858vl[cs.CL]14Sep2026

Recursiveself-improvementisbecomingincreasinglyvitalforautonomousAIagents,whereprogresshingesondiscoveringhigh-valuesolutionsacrosscomplexdomains.Thedriverofthisprocessiseffec-tiveexploration,however,managingandimprovingexplorationstrategiesremainsamajorbottleneck.Currentsystemsfaceafundamentaldilemma:fixedstrategiesfailtoadaptassearchspacesscale,whileonlinepolicyoptimizationrequiresnavigatingvastmeta-searchspacesunderdelayedandexpensivefeedbackoverlong-horizonrollouts.WeintroduceDREAM-RSI,aframeworkforscalableandrecur-sivelyself-improvingexploration.Alightweightorchestrationlayermakesexplorationexplicitandprogrammablewhileleavingtheunderlyingcodingagentunchanged.Ourkeyinsightisthataccumu-lateddiscoveryhistorycanserveasareplaysimulatorovertherealizedsearchspace.Byperformingdreaminginthereplaysimulatorconstructedfromhistoricaldiscoverytrees,DREAM-RSIsecuresimmediate,low-costoff-policyfeedbacktoevaluateandrefineexplorationpolicieswithoutinvokingrepetitive,expensiveonlineevaluations.Theimprovedpolicyissubsequentlyredeployedonlinetodrivefurtherdiscovery,continuouslyexpandingthesimulatorpoolinaself-improvingloop.Acrossalgorithmengineering,mathematicaloptimization,andGPUkernelengineering,DREAM-RSIachievescompetitiveorimproveddiscoveryqualitywhilesubstantiallyreducingdiscoverycostinseveralsettings.

github.com/zhengkid/Dream-RSIdream-rsi.com

1.Introduction

Recursiveself-improvement(RSI)hasemergedasanambitiousgoalforautonomousAIsystems(Liuetal.,2026c).AcommonmechanismunderlyingRSIisaniterativediscoveryloopwhereinagentsgeneratecandidatesolutions,evaluateoutcomes,incorporatefeedback,andrefinefutureiterations.Suchdiscoveryloopshavedrivensubstantialprogressacrossscientificandalgorithmicdomains,includingalgorithmdesign(Novikovetal.,2025;Romera-Paredesetal.,2024),open-endedmath-ematicaloptimization(Anthropic,2026;Georgievetal.,2025),systemsdesign(Caoetal.,2026;JaberandJaber,2026),andagentself-improvement(Leeetal.,2026;Zhangetal.,2026b,c;Zhengetal.,2026a),withthesediscoveriesincreasinglyfeedingintothedevelopmentofmorecapableAIsystems.Asagentcapabilitiesimproveandself-improvementtargetsbecomechallenging,discoveryincreasinglyrequireslong-horizonexplorationovervastsearchspaces,oftenspanningthousandsofproposal-evaluationcycles(OpenAI,2026;Yeetal.,2026).Atthisscale,theabilitytoorchestrateex-plorationbecomescritical(Zhengetal.,2026b).Poorexplorationcanwastesubstantialcomputationandtime,severelylimitingtheefficiencyandscalabilityofRSI.

Existingapproacheshavelargelyreliedonmanuallydesignedexplorationstrategiesthatremainlargelyfixedthroughoutdiscovery(Duetal.,2026;Jiangetal.,2026;Novikovetal.,2025;Yanetal.,2026b;Yeetal.,2026).Fixedstrategiescannotimprovefromaccumulateddiscoveryexperienceandmayrepeatedlyallocatecomputationtoineffectivesearchdirections.Recentworkthereforeseekstooptimizeexplorationpoliciesonlineduringdiscovery(Liuetal.,2026a),butdoingsofaces

Correspondingauthor(s):xidongwu@,zzhangx@

◎2026Google.Allrightsreserved

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

UpdatePolicy

Exploration

policy

①OnlineExplore

ReplaySimulator

Simulator

DiscoveryTree

AllpreviousHistory

StoretoHistoryscaling

③Dreaming-basedPolicyImprovement

Update

Interact

SimulatorPool

Evaluate

FeedbackSimulatorPool

Figure1|OverviewofDREAM-RSI.Thesystemoperatesinarecursiveself-improvementloopviathreecorestages:①OnlineExplore,wherethecurrentexplorationpolicyguidesacodingagenttoexpandadiscoverytreeandloghistoricaltraces;②ConstructReplaySimulator,wherethegenerateddiscoverytreeisconvertedintoareusablesimulatorpool;and③Dreaming-basedPolicyImprovement,wheretheagent"dreams"upamassivepoolofalternativepoliciesinitsmind.Itthenfeedsthesecandidatepoliciesintothereplaysimulatortosimulateexecutionsandderiverapidfeedback,continuouslyrefiningitsstrategy(detailedintheZoom-inbox).Theupdatedpolicythenredeploysforthenextroundofonlineexploration.

twofundamentalbottlenecks.First,feedbackisdelayedandexpensiveatthemetalevel:unlikeevaluatinganindividualcandidate,assessinganexplorationpolicyrequiresobservinghowitshapesthesubsequentdiscoveryprocessovermanyproposal-evaluationcycles.Second,themeta-policyspaceisvast:anewlyproposedpolicymayperformpoorly,somanyalternativesmayneedtobetried.Together,thesechallengesmakemeta-levelimprovementparticularlycostly:eachpolicymayrequirealongonlinerolloutbeforereceivingusefulfeedback,makingitdifficulttoefficientlyclosetheself-improvementloopattheexplorationlayer.

Toaddressthesebottlenecks,ourkeyintuitionissimple:afastandinexpensivesimulatorofdiscoverywouldallowmanyexplorationpoliciestobeevaluatedbeforecostlyonlinedeployment.Surprisingly,completeddiscoveryhistoriesalreadyprovidesuchasimulator.Whilepriorworktreatspastdiscoveryhistorymerelyasstatictextualcontext(Huetal.,2025;Ouyangetal.,2026b)ortrainingdataforweightfine-tuning(Wangetal.,2025;Yuksekgonuletal.,2026),acompleteddiscoveryprocessinherentlyrecordsastructuredtreeofpastexplorationdecisionsandtheirrealizedcode-executionoutcomes.Drawingananalogytomodel-basedreinforcementlearningandWorldModels(HaandSchmidhuber,2018;Hafneretal.,2023)(§2),onceorganizedintoadiscoverytree,thishistorycanserveasareplaysimulator¹.AsillustratedinFigure2,analternativeexplorationstrategycannavigatethispre-recordedtreetotraversedifferentsubsetsofrecordedbranches,indifferentorders,withdifferentparallelgroupingsandstoppingdecisions.Becauseallexecutionoutcomesarealreadysavedinthetree,evaluatinganewstrategyrequiresonlyreadingpastrecordswithoutrerunningtheunderlyingdiscoveryagentorevaluator.Thistransformsmeta-policyimprovementfromanexpensiveonlinetrial-and-errorprocessintoafast,simulation-based“dreaming”

¹Weusethetermsreplaysimulatorandworldsinterchangeably.

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

3

procedure.

Buildingonthisinsight,weintroduceDREAM-RSI,aframeworkforscalableandrecursivelyself-improvingmeta-explorationinagent-drivendiscovery.Wefirstmakeexplorationexplicitandprogrammablethroughalightweightorchestrationlayerthatcontrolsbranching,parallelexploration,andstoppingwhileleavingtheunderlyingcodingagentunchanged.Ratherthankeepingthispolicyfixed,DREAM-RSIestablishesaclosed-loopself-improvementmechanismacrossthreecorestages(Figure1):(1)OnlineExploration,wherethecurrentpolicyguidesreal-worlddiscoveryandlogshistoricalexecutiontraces;(2)SimulatorConstruction,whererecordeddiscoverytreesareconvertedintoareusablereplaysimulatorpool;and(3)Dreaming-basedPolicyImprovement,wherecandidatepoliciesareevaluatedvialow-cost"dreaming"overthesimulator.Theupdatedpolicyisthenredeployedonlinetogeneratenewdiscoveryexperienceandexpandthesimulatorpool,closingaRSIloopatthemeta-explorationlayer.

Empirically,weevaluateDREAM-RSIacross8scientificdiscoverytasksspanningthreedistinctdo-mains:algorithmengineering,mathematicaloptimization,andGPUkernelengineering.Inalgorithmengineering(Lassopathsolver),DREAM-RSIoutperformsstandardlibrarieslikesklearnandstrongbaselineswhilereducingagentcallsbyupto162×overSIMPLETESand1.7×overfixed-explorationbaselines.Inmathematicaloptimization(sum-difference,autocorrelation,circlepacking),itmatchesorsurpassesstrongbaselineswithin1kgenerations,yieldingover50×budgetsavingscomparedtoSIMPLETES.InGPUkernelengineering(KernelBench),iteitherreachestargetexecutionspeedsusing1.79×-2.43×fewergenerationsorimproveskernelperformancebyupto2.09×underidenticalbudgetconstraints.

Insummary,ourmaincontributionsareasfollows:1)HistoryasReplaySimulator:Weconcep-tualizecompleteddiscoveryhistoriesasreplaysimulators.Thismakesdelayedexplorationfeedbackreusableforefficientmeta-explorationpolicyevaluation.;2)Meta-LayerRSILoop(DREAM-RSI):WeintroduceDREAM-RSI,establishingarecursiveself-improvementloopthatcontinuouslycollectsdiscoveryhistoriesthroughonlineexploration,constructsreplaysimulatorsfromhistorytorefinemeta-explorationstrategiesviadreaming,andredeploystheupgradedpolicyonline;3)EmpiricalValidation:WeconductexperimentstodemonstratethatDREAM-RSIimprovesbothdiscoveryeffectivenessandefficiencyinseveralsettings.

2.Motivation:DiscoveryHistoryasaReplaySimulator

Consideranagentnavigatingtowardagoalinanunfamiliarenvironment.Duringitsfirsttraversal,theagentmayfollowinefficientroutes,encounterdeadends,backtrack,andgraduallyconstructamapofthesurroundingspace.Oncerecorded,however,thisexperiencebecomesreusable:theresultingmapsupportsplanningwithoutrequiringtheagenttophysicallyrevisiteverylocation.Anewnavigationpolicycaninsteadreasonovertheaccumulatedmap,avoidknowndeadends,reconsiderearlierdecisions,andcomparealternativeroutesbeforeacting(Guptaetal.,2017).

Thisideaparallelsmodel-basedreinforcementlearning(M.Moerlandetal.,2023;Sutton,1990).

Amodelcaptureshowanenvironmentevolvesinresponsetoanagent'sactions,allowingpoliciestobetrainedorevaluatedthroughsimulatedexperienceratherthanrepeatedinteractionwiththerealenvironment(HaandSchmidhuber,2018).TheDreamerfamily(Hafneretal.,2019,2020,2023,2025)demonstratesthisprincipleparticularlyclearly:anagentlearnsacompactdynamicsmodelfromcollectedexperienceandimprovesitspolicybyimaginingtrajectorieswithinthatmodel.

Long-horizondiscoveryadmitsananalogousstructure.Anexplorationpolicydecideswhichdirectionstopursue,whichcandidatestorefine,whichbranchestoexploreinparallel,andwhen

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

SelectedPolicyAllpreviousHistory

OnlineExplore

ReplayFeedback

(Quality1;Cost↓;Latency↓)

Score(AlternativePolicy1)

Score(AlternativePolicy2)

ExplorationPolicyDistribution

AlternativePolicy1

(Replay)

AlternativePolicy2

(Replay)

DiscoveryHistory(Simulator)

Figure2|Discoveryhistoryasareplaysimulator.Adeployedpolicyfirstexploresonlinetogenerateastructureddiscoverytreecontaininghistoricalexecutiontraces(eachnodedenoteanattemptwithitsfullobservation).Thousandsofcandidatepoliciescanthenbetestedwithinthissimulator—evaluatingalternativechoicesofsearchbranches,explorationorders,concurrencylevels,andstoppingrules.Sinceallnodeoutcomesarepre-stored,asinglecostlyonlinerunenablesthousandsofrapid,zero-execution-costoff-policyevaluations.Thisenablespolicyimprovementthroughhistoricalreplay:theagentcan“dream”overmanyalternativeexplorationstrategiesbeforeredeployingtheimprovedpolicyonline.

toterminate.Executingthepolicyonlineproducesastructureddiscoveryhistorycontainingtheexploredbranches,decisionpoints,computationalcosts,andrealizedoutcomes.AsillustratedinFigure2,thishistorycansubsequentlybetreatedasanempiricalreplaysimulator:agroundedmodeloftheportionofthediscoveryspacethathasalreadybeenobserved.

Withinthisreplaysimulator,alternativeexplorationpoliciesinducedifferenttrajectoriesthroughtherecordeddiscoverytree.Apolicymayselectadifferentsubsetofbranches,prioritizetheminadifferentorder,issuedifferentrequestsinparallel,orstopatanearlierpoint.Evaluatingsuchatrajectoryrequiresonlyrevealingtheoutcomesalreadystoredalongtheselectedbranches,ratherthanrerunningtheunderlyingcodingagentandevaluator.Consequently,asingleexpensiveonlinediscoveryruncansupportmanyinexpensiveevaluationsofalternativeexplorationstrategies.

3.DREAM-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

AsshowninFigure1,DREAM-RSIalternatesbetweenonlineexplorationandoffline“dreaming”toimproveanexecutableexplorationpolicythatallocatesdiscoverycomputation.Duringtheonlinephase,thepolicyguidesafixeddiscoveryagent,whileafixedevaluatorscorestheresultingcandidatesandprovidesdiagnosticfeedback.Theresultingdiscoverytreeservesasareplayworldinwhichalternativepoliciescanbeevaluatedusingrecordedoutcomes.AfixedLLM-basedpolicy-developmentagentusesthisfeedbacktorevisetheexplorationpolicycode,andthebestevaluatedversionisdeployedforthenextonlinerollout.Onlytheexploration-policycodechanges;theunderlyingmodels,evaluator,andexecutioninterfacesremainfixed.

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

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Discoverytreesandtheshareddecisioninterface.Adiscoverytreeisrootedatr,whichrepresentstheinitialworkspacestate.Eachnon-rootnodevhasexactlyoneprimaryparent,eithertherootorapreviouslycreatednode.Thisparentidentifieswheretheattemptinvbegins:thediscoveryagentresumestheparent'ssavedworkspaceandusesitsaccumulatedobservationsascontexttoproduceanewattempt.Nodevpreservesthisinheritedhistoryandrecordstheoutcomeofthenewgeneration-evaluationattempt,includingtheresultingfilesystemsnapshot,generatedartifact,evaluationdiagnostics,andscoresv.Scoresfollowafixedtask-scoringprotocol,withlargervaluesindicatingbetterquality.

Inbothonlineexecutionandofflinereplay,theexplorationpolicyobservesatreeT,initiallycontainingonlytheroot,andselectsthenodesfromwhichtocontinueexploration.TheeligiblenodesformthesetA(T)={r}U{v∈T:visaleaf},whereleavesaredeterminedfromthecurrentlyobservedtree.LetW≥1bethenumberofparallelworkers,eachofwhichcanexecuteonegeneration-evaluationrequestatatime(e.g.concurrentAPIcalls).Theexplorationpolicy'sactionisabatchC∈A(T;W),whereisthefeasiblebatchset.Eachselectednodespecifiesthestartingpointofoneattempt,sothebatchdeterminesbothwhereexplorationcontinuesandhowmanyattemptsarescheduledinparallel.Boththeonlineandofflinephasesusethissamedecisioninterfacebutdifferinthetransitionthatfollowsaselectedbatch.

Onlinerollout.Lett=1,2,...indextheouteriterations,startingfromaninitialpolicyπ1andanemptyhistoryHo=().Atiterationt,policyπtguidesanewonlinerolloutwithaccesstothecompleteddiscoveryhistoryH-1.Thishistoryprovidescontextforexplorationbutremainsseparatefromthenewtreebeingconstructed.Thepolicycodestaysfixedthroughouttherollout.

Letdenotethenewdiscoverytreeafterkcompleteddecisionrounds,with.TherolloutallowsatmostK₁rounds.Atroundk≤K1,theexplorationpolicychoosesanodebatch(andeachnodeiisassignedtoaworker.Thediscoveryagentusesv'ssavedworkspaceandavailablecontexttoproduceanewcandidate,andtheevaluatorassessestheresult.Theseattemptsruninparallel,eachproducingonenewchildofitsselectedparent.Attachingthecompletedchildrentothecurrenttreeyields,whileallpreviouslyrecordednodesremainunchanged.Thistransitionisstochasticbecausethediscoveryagentmaygeneratedifferentoutcomesfromthesamestartingworkspace.Forthenextround,thenewlycreatedchildbecomestheselectableleafofanextendedbranch,whiletherootremainsselectableforopeningfurtherbranches.TherolloutendswhenthepolicyselectsanemptybatchorcompletesK₁decisionrounds.Aftertherolloutterminates,itsfinaltreeisrecordedasTtandappendedtothehistory,givingH=H-1U{π}.Themethodthenenterstheofflinephaseusingthisexpandedcollectionofreplayworlds.

Offlineevaluation.Duringtheofflinephaseofouteriterationt,thehistoryHremainsfixedwhilethemethodconstructsandevaluatesM≥1policyversions,startingwithEachversionisevaluatedseparatelyoneveryhistoricaltreebeforethenextversionisdevelopedfromtheresultingfeedback.Weusemtoindexpolicyversions,itoindexreplayworlds,andktocountdecisionroundswithinonepolicy-worldevaluation.Theouterindextisfixedthroughoutthisphaseandissuppressedinthenotationforreplaytrajectoriesandscores.

Foreachpolicy-treepair(m,i),replayresetsthepolicy'sper-rolloutstateandstartsfrom

{r}.Here,denotesthesubtreerevealedafterkcompletedrounds.Thefullrecordedtree

Tiremainsfixed;onlytheportionobservedbythepolicyevolves.Ateachdecision,π"selectsa

batch)usingtherevealedobservations.Unlikeonlineexecution,replayreturns

recordedchildrenoftheselectednodesdeterministicallyratherthangeneratingnewcandidates.

Aftertheexplorationpolicytakesanonemptybatch,thenextobservedtreeis

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

6

)whereChild()denotesthenodesetcontainingunobservedchildrenofvontreeTigiventhecurrentobservedtree.Forv≠r,Child(v;T,Tm,k)isv'suniquerecordedchild,ifoneexists.Sincevisaleafof,thatchildisstillunrevealed.Forv=r,replayreturnstheearliest-createdchildofroutside,openingonepreviouslyunrevealedbranch.Ineithercase,Child(v;Ti,whennorecordedcontinuationremains.Thenewlyrevealednodesexposetheirstoredobservationsbeforethepolicymakesitsnextdecision.

ReplayallowsatmostK₂decisionroundswhereeachnonemptybatchcountsasoneround,andterminateswhenthepolicyselects,theroundlimitk=K₂isreached,or,meaningthatallrecordednodeshavebeenrevealed.Letdenotethenumberofcompleted

roundsattermination,yieldingthefinalsubtree

Thus,replayevaluateshowfartopursueeachopenedbranch,howtogroupattemptsintoparallelbatches,andwhentoopenanotherbranchorstop.Thesedecisionsmaydifferacrosspolicies,buteachbranchistraversedinitsrecordedparent-childorder,andnooutcomesbeyondTiaregenerated.

Replayobjective.Thereplayobjectivebalancesdiscoveryquality,executioncost,andparallelism.LetIbethenumberofrevealednon-rootnodes.Althoughreplayitselfdoesnotexecutenewdiscoveryattempts,countsthegeneration-evaluationrequestsrepresentedbyitstrajectory.Forfixedcoefficientsβ1,β2≥0,thereplayscoreis

(1)

Thefirsttermmeasuresthebestsolutionqualityattainedduringreplay.Thesecondpenalizesthenumberofattemptedgenerations.Foranonemptyreplay,thethirdrewardstheaveragenumberofattemptsexecutedperdecisionround,favoringpoliciesthatbatchusefulcontinuationsratherthanexecutethemsequentially.

Policyimprovementandselection.Theevaluationscoreofpolicyversionisitsaveragereplay

scoreacrossthefixedhistory,.Theofflinephasebeginsbyevaluatingthecurrentpolicy

.Foreachm=0,...,M-1,thepolicy-developmentagentexaminesthereplaytrajectories

andscoresof:,togetherwithfeedbackfromearlierrevisions,toidentifysuccessfuldecisionsand

recurringfailures.Itthenrevisestheexecutablepolicycodetoproduce,whichisevaluatedon

thesametreplayworlds.Replayfeedbackisavailabletothedevelopmentagentbetweenrevisions.

AfterMrevisions,thenextonlinepolicyisselectedfromallMevaluatedversionsaswherem*∈argmaxme{0.…,M-1}Vm.Becausethecandidatesetincludesthecurrentpolicy,thisselectionsatisfiesvm*≥v⁰.Thus,theselectedpolicyπt+1isnoworsethanthecurrentpolicyπtinaveragereplayscoreonthefixedhistoryH.TheselectedpolicyisthendeployedonlinetocollectTt+1,expandingthehistoryavailableforthenextofflineimprovementphase.

4.Experiments

WeevaluateDREAM-RSIacrossthreescientificdiscoverydomains:algorithmengineering,kerneloptimizationandmathoptimization.OurprimarycontrolledbaselineisRecursiveFixedExploration,

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

7

whichusesthesameunderlyingdiscoverysettingandinitializationbutkeepstheexplorationpolicyfixedacrossrecursivediscoveryrounds.Weadditionallycompareagainsttask-specificdomainbaselines.

Acrossalltasks,DREAM-RSIandRecursiveFixedExplorationusethesamediscoveryagent,evaluator,initialization,andresourceconstraints.Bothmethodsstartfromthesamemanuallydesignedexplorationpolicy.Thisexplorationpolicyfollowsasimpleparallelrefiningstrategy:itlaunchesmultipleindependentexplorationworkspacesinparallel,witheachworkspacemaintainingitsownlocaldiscoverytrajectoryandrepeatedlyrefiningitscurrentcandidatebasedonthehistoryaccumulatedwithinthatworkspace.Thetwomethodsthereforefollowthesameexplorationpolicyinthefirstdiscoveryround.Insubsequentrounds,whileRecursiveFixedExplorationkeepsitsexplorationpolicystatic,DREAM-RSIprogressivelyrefinesthepolicybydreamingoverareplaysimulatorconditionedonaccumulatedglobaldiscoveryhistory,subsequentlydeployingtheupdatedpolicyineachnewround.Thediscoverycostisquantifiedbythetotalcumulativenumberofdiscovery-agentcalls.

Specifically,weevaluateGemini-3.1ProandGemini-3.7-FlashacrossmultiplerecursivediscoveryroundsviatheGeminiCLI².UnderRecursiveFixedExploration,eachroundforGemini-3.1Proexecutes10parallelworkspaceswithupto11refinementsteps(10×11=110discovery-agentcalls),whereasGemini-3.7-Flashoperates32parallelworkspaceswithupto20refinementsteps(32×20=640calls).DREAM-RSImaintainsidenticalper-roundbudgets,aligningwiththebaselineinRound1whileprogressivelyupdatingitspolicyinsubsequentrounds.Furtherdetailsonrecursiverounds,tasksetups,resourcebudgets,andevaluationprotocolsfollowbelow.

4.1.AlgorithmEngineering

Inthistask,weconsiderLassoRegularizationPathasouralgorithm-engineeringtask,afundamentalcomputationalprimitiveinhigh-dimensionalstatisticsthatiswidelyusedinmodelselectionandcross-validationacrossdomainssuchasgenomicsandfinance.WefollowthebenchmarksettingofSimpleTES(Yeetal.,2026),wherethegoalistodiscoverefficientimplementationsofthecompleteLassoregularizationpathwhilepreservingnumericalcorrectness.Duringdiscovery,weusethesame17syntheticinstancesasSimpleTES,whichcoverdiverseproblemregimesintermsofdimensionality,sparsity,featurecorrelation,andactive-setstructure.Toevaluatewhetherthediscoveredalgorithmsgeneralizebeyondthesearchdistribution,weadditionallyevaluatethemonsixheld-outdownstreamdatasetsspanningbothbiologicalandnon-biologicaldomains.

BaselinesandSetup.WecompareagainststandardLassosolverssklearn(Pedregosaetal.,2011)andglmnet(Friedmanetal.,2010),aswellasSimpleTES(Yeetal.,2026),whichusesGPT-OSS-120Bwithareportedbudgetof51,200generations.WeadditionallyincludeRecursiveFixedExplorationasourcontrolledbaseline.Specifically,werunbothRecursiveFixedExplorationandDREAM-RSIfor5rounds.

MainResults.Figure3(a)summarizestheLassodiscoveryresults.Acrossbothdiscovery-agentbackbones,DREAM-RSIachievesabetterdownstreamquality-computetrade-offthanRecursiveFixedExploration.WithGemini-3.1Pro,itreducestheaverageruntimeacrossthesixheld-outdatasetsfrom3587.1msto2931.0mswhileusingonly317discovery-agentcalls,comparedwith550callsforfixedexploration.WithGemini-3.7-Flash,DREAM-RSIfurtherreducestheaverageruntimefrom2516.7msto2350.6msusing1879callsinsteadof3200.Despiteusingsubstantiallylessdiscoverycompute,

²

/

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

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MethodModelCompute

Non-biologicalBiological

Avg.

GisetteRCV1DNALeukemiaColonDukeBreast

Previoussolvers

sklearn

11275.2

252881.7

93.8

227.2

229.8

374.0

44180.3

glmnet

-

9063.6

73072.8

351.9

45.0

24.2

47.7

13767.5

SimpleTES

gpt-oss-120b

51,200

3141.9

19625.6

15.9

15.5

11.6

18.1

3804.8

SimpleTES÷

gpt-oss-120b

51,200

8651.0

41143.1

37.6

28.2

19.5

31.1

8318.4

OurSystem

RecursiveFixedExploration

Gemini-3.1-Pro

550

1861.8

19550.1

41.5

26.1

14.5

28.4

3587.1

Gemini-3.7-Flash

3200

1133.1

13873.0

29.8

24.1

15.7

24.4

2516.7

DREAM-RSI

Gemini-3.1-Pro

317

2841.0

14616.0

49.9

30.2

16.4

32.5

2931.0

Gemini-3.7-Flash

1879

1091.9

12923.4

31.4

21.0

12.2

23.6

2350.6

(a)Finalperformance.

Gemini-3.1-Pro

vg.Runtime(ms)

100300

DiscoveryCompute

Gemini-3.7-Flash

DiscoveryCompute

RecursiveFixedExplorationDREAM-RSI

(b)RecursiveDiscoveryDynamics.

Figure3|Lassoregularization-pathdiscoveryresults.(a)Finalwall-clockruntimeonsixheld-outdownstreamtasks;lowerisbetter.Computedenotesthecumulativenumberofdiscovery-agentcalls.(b)Recursivediscoverydynamics.Averagedownstreamruntimeacrosssixheld-outtasksversuscumulativediscoverycomputeforGemini-3.1-ProandGemini-3.7-Flash.Numbersnexttomarkersdenoterecursiverounds(iterations).Lowerisbetter.

theresultingsolversalsooutperformthestandardsklearnandglmnetimplementationsonallsixheld-outdatasets.ComparedwithSimpleTES,whichuses51,200generations,DREAM-RSIachievesloweraveragedownstreamruntimewithroughlytwoordersofmagnitudefewerdiscovery-agentcalls.Notably,theprogramdiscov