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

Recursiveself-improvementisbecomingincreasinglyvitalforautonomousAIagents,whereprogress

hingesondiscoveringhigh-valuesolutionsacrosscomplexdomains.Thedriverofthisprocessiseffec-

tiveexploration,however,managingandimprovingexplorationstrategiesremainsamajorbottleneck.

Currentsystemsfaceafundamentaldilemma:fixedstrategiesfailtoadaptassearchspacesscale,while

onlinepolicyoptimizationrequiresnavigatingvastmeta-searchspacesunderdelayedandexpensive

feedbackoverlong-horizonrollouts.WeintroduceDREAM-RSI,aframeworkforscalableandrecur-

sivelyself-improvingexploration.Alightweightorchestrationlayermakesexplorationexplicitand

programmablewhileleavingtheunderlyingcodingagentunchanged.Ourkeyinsightisthataccumu-

lateddiscoveryhistorycanserveasareplaysimulatorovertherealizedsearchspace.Byperforming

dreaminginthereplaysimulatorconstructedfromhistoricaldiscoverytrees,DREAM-RSIsecures

immediate,low-costoff-policyfeedbacktoevaluateandrefineexplorationpolicieswithoutinvoking

repetitive,expensiveonlineevaluations.Theimprovedpolicyissubsequentlyredeployedonlinetodrive

furtherdiscovery,continuouslyexpandingthesimulatorpoolinaself-improvingloop.Acrossalgorithm

engineering,mathematicaloptimization,andGPUkernelengineering,DREAM-RSIachievescompetitive

orimproveddiscoveryqualitywhilesubstantiallyreducingdiscoverycostinseveralsettings.

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

1.Introduction

Recursiveself-improvement(RSI)hasemergedasanambitiousgoalforautonomousAIsystems(Liu

etal.,2026c).AcommonmechanismunderlyingRSIisaniterativediscoveryloopwhereinagents

generatecandidatesolutions,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;Zheng

etal.,2026a),withthesediscoveriesincreasinglyfeedingintothedevelopmentofmorecapableAI

systems.Asagentcapabilitiesimproveandself-improvementtargetsbecomechallenging,discovery

increasinglyrequireslong-horizonexplorationovervastsearchspaces,oftenspanningthousandsof

proposal-evaluationcycles(OpenAI,2026;Yeetal.,2026).Atthisscale,theabilitytoorchestrateex-

plorationbecomescritical(Zhengetal.,2026b).Poorexplorationcanwastesubstantialcomputation

andtime,severelylimitingtheefficiencyandscalabilityofRSI.

Existingapproacheshavelargelyreliedonmanuallydesignedexplorationstrategiesthatremain

largelyfixedthroughoutdiscovery(Duetal.,2026;Jiangetal.,2026;Novikovetal.,2025;Yanetal.,

2026b;Yeetal.,2026).Fixedstrategiescannotimprovefromaccumulateddiscoveryexperience

andmayrepeatedlyallocatecomputationtoineffectivesearchdirections.Recentworktherefore

seekstooptimizeexplorationpoliciesonlineduringdiscovery(Liuetal.,2026a),butdoingsofaces

Correspondingauthor(s):xidongwu@,zzhangx@

◎2026Google.Allrightsreserved

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

UpdatePolicy

Exploration

policy

③Dreaming-basedPolicyImprovement

①OnlineExplore

ReplaySimulatorUpdate

Interact

SimulatorSimulatorPool

DiscoveryTree

AllpreviousHistory

Evaluate

SimulatorPool

StoretoHistoryscalingFeedback

Figure1|OverviewofDREAM-RSI.Thesystemoperatesinarecursiveself-improvementloopvia

threecorestages:①OnlineExplore,wherethecurrentexplorationpolicyguidesacodingagent

toexpandadiscoverytreeandloghistoricaltraces;②ConstructReplaySimulator,wherethe

generateddiscoverytreeisconvertedintoareusablesimulatorpool;and③Dreaming-basedPolicy

Improvement,wheretheagent"dreams"upamassivepoolofalternativepoliciesinitsmind.It

thenfeedsthesecandidatepoliciesintothereplaysimulatortosimulateexecutionsandderiverapid

feedback,continuouslyrefiningitsstrategy(detailedintheZoom-inbox).Theupdatedpolicythen

redeploysforthenextroundofonlineexploration.

twofundamentalbottlenecks.First,feedbackisdelayedandexpensiveatthemetalevel:unlike

evaluatinganindividualcandidate,assessinganexplorationpolicyrequiresobservinghowitshapes

thesubsequentdiscoveryprocessovermanyproposal-evaluationcycles.Second,themeta-policy

spaceisvast:anewlyproposedpolicymayperformpoorly,somanyalternativesmayneedtobe

tried.Together,thesechallengesmakemeta-levelimprovementparticularlycostly:eachpolicymay

requirealongonlinerolloutbeforereceivingusefulfeedback,makingitdifficulttoefficientlyclose

theself-improvementloopattheexplorationlayer.

Toaddressthesebottlenecks,ourkeyintuitionissimple:afastandinexpensivesimulatorof

discoverywouldallowmanyexplorationpoliciestobeevaluatedbeforecostlyonlinedeployment.

Surprisingly,completeddiscoveryhistoriesalreadyprovidesuchasimulator.Whilepriorwork

treatspastdiscoveryhistorymerelyasstatictextualcontext(Huetal.,2025;Ouyangetal.,2026b)

ortrainingdataforweightfine-tuning(Wangetal.,2025;Yuksekgonuletal.,2026),acompleted

discoveryprocessinherentlyrecordsastructuredtreeofpastexplorationdecisionsandtheirrealized

code-executionoutcomes.Drawingananalogytomodel-basedreinforcementlearningandWorld

Models(HaandSchmidhuber,2018;Hafneretal.,2023)(§2),onceorganizedintoadiscovery

tree,thishistorycanserveasareplaysimulator¹.AsillustratedinFigure2,analternative

explorationstrategycannavigatethispre-recordedtreetotraversedifferentsubsetsofrecorded

branches,indifferentorders,withdifferentparallelgroupingsandstoppingdecisions.Becauseall

executionoutcomesarealreadysavedinthetree,evaluatinganewstrategyrequiresonlyreadingpast

recordswithoutrerunningtheunderlyingdiscoveryagentorevaluator.Thistransformsmeta-policy

improvementfromanexpensiveonlinetrial-and-errorprocessintoafast,simulation-based“dreaming”

¹Weusethetermsreplaysimulatorandworldsinterchangeably.

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

procedure.

Buildingonthisinsight,weintroduceDREAM-RSI,aframeworkforscalableandrecursively

self-improvingmeta-explorationinagent-drivendiscovery.Wefirstmakeexplorationexplicitand

programmablethroughalightweightorchestrationlayerthatcontrolsbranching,parallelexploration,

andstoppingwhileleavingtheunderlyingcodingagentunchanged.Ratherthankeepingthispolicy

fixed,DREAM-RSIestablishesaclosed-loopself-improvementmechanismacrossthreecorestages

(Figure1):(1)OnlineExploration,wherethecurrentpolicyguidesreal-worlddiscoveryand

logshistoricalexecutiontraces;(2)SimulatorConstruction,whererecordeddiscoverytreesare

convertedintoareusablereplaysimulatorpool;and(3)Dreaming-basedPolicyImprovement,

wherecandidatepoliciesareevaluatedvialow-cost"dreaming"overthesimulator.Theupdated

policyisthenredeployedonlinetogeneratenewdiscoveryexperienceandexpandthesimulatorpool,

closingaRSIloopatthemeta-explorationlayer.

Empirically,weevaluateDREAM-RSIacross8scientificdiscoverytasksspanningthreedistinctdo-

mains:algorithmengineering,mathematicaloptimization,andGPUkernelengineering.Inalgorithm

engineering(Lassopathsolver),DREAM-RSIoutperformsstandardlibrarieslikesklearnandstrong

baselineswhilereducingagentcallsbyupto162×overSIMPLETESand1.7×overfixed-exploration

baselines.Inmathematicaloptimization(sum-difference,autocorrelation,circlepacking),itmatches

orsurpassesstrongbaselineswithin1kgenerations,yieldingover50×budgetsavingscomparedto

SIMPLETES.InGPUkernelengineering(KernelBench),iteitherreachestargetexecutionspeeds

using1.79×-2.43×fewergenerationsorimproveskernelperformancebyupto2.09×underidentical

budgetconstraints.

Insummary,ourmaincontributionsareasfollows:1)HistoryasReplaySimulator:Weconcep-

tualizecompleteddiscoveryhistoriesasreplaysimulators.Thismakesdelayedexplorationfeedback

reusableforefficientmeta-explorationpolicyevaluation.;2)Meta-LayerRSILoop(DREAM-RSI):

WeintroduceDREAM-RSI,establishingarecursiveself-improvementloopthatcontinuouslycollects

discoveryhistoriesthroughonlineexploration,constructsreplaysimulatorsfromhistorytorefine

meta-explorationstrategiesviadreaming,andredeploystheupgradedpolicyonline;3)Empirical

Validation:WeconductexperimentstodemonstratethatDREAM-RSIimprovesbothdiscovery

effectivenessandefficiencyinseveralsettings.

2.Motivation:DiscoveryHistoryasaReplaySimulator

Consideranagentnavigatingtowardagoalinanunfamiliarenvironment.Duringitsfirsttraversal,

theagentmayfollowinefficientroutes,encounterdeadends,backtrack,andgraduallyconstruct

amapofthesurroundingspace.Oncerecorded,however,thisexperiencebecomesreusable:the

resultingmapsupportsplanningwithoutrequiringtheagenttophysicallyrevisiteverylocation.

Anewnavigationpolicycaninsteadreasonovertheaccumulatedmap,avoidknowndeadends,

reconsiderearlierdecisions,andcomparealternativeroutesbeforeacting(Guptaetal.,2017).

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

Amodelcaptureshowanenvironmentevolvesinresponsetoanagent'sactions,allowingpoliciesto

betrainedorevaluatedthroughsimulatedexperienceratherthanrepeatedinteractionwiththereal

environment(HaandSchmidhuber,2018).TheDreamerfamily(Hafneretal.,2019,2020,2023,

2025)demonstratesthisprincipleparticularlyclearly:anagentlearnsacompactdynamicsmodel

fromcollectedexperienceandimprovesitspolicybyimaginingtrajectorieswithinthatmodel.

Long-horizondiscoveryadmitsananalogousstructure.Anexplorationpolicydecideswhich

directionstopursue,whichcandidatestorefine,whichbranchestoexploreinparallel,andwhen

3

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

SelectedPolicyAllpreviousHistory

OnlineExplore

ReplayFeedback

(Quality1;Cost↓;Latency↓)

AlternativePolicy1Score(AlternativePolicy1)

(Replay)

AlternativePolicy2Score(AlternativePolicy2)

(Replay)

ExplorationPolicy

Distribution

DiscoveryHistory(Simulator)

Figure2|Discoveryhistoryasareplaysimulator.Adeployedpolicyfirstexploresonlineto

generateastructureddiscoverytreecontaininghistoricalexecutiontraces(eachnodedenotean

attemptwithitsfullobservation).Thousandsofcandidatepoliciescanthenbetestedwithinthis

simulator—evaluatingalternativechoicesofsearchbranches,explorationorders,concurrencylevels,

andstoppingrules.Sinceallnodeoutcomesarepre-stored,asinglecostlyonlinerunenables

thousandsofrapid,zero-execution-costoff-policyevaluations.Thisenablespolicyimprovement

throughhistoricalreplay:theagentcan“dream”overmanyalternativeexplorationstrategies

beforeredeployingtheimprovedpolicyonline.

toterminate.Executingthepolicyonlineproducesastructureddiscoveryhistorycontainingthe

exploredbranches,decisionpoints,computationalcosts,andrealizedoutcomes.Asillustratedin

Figure2,thishistorycansubsequentlybetreatedasanempiricalreplaysimulator:agroundedmodel

oftheportionofthediscoveryspacethathasalreadybeenobserved.

Withinthisreplaysimulator,alternativeexplorationpoliciesinducedifferenttrajectoriesthrough

therecordeddiscoverytree.Apolicymayselectadifferentsubsetofbranches,prioritizethemin

adifferentorder,issuedifferentrequestsinparallel,orstopatanearlierpoint.Evaluatingsucha

trajectoryrequiresonlyrevealingtheoutcomesalreadystoredalongtheselectedbranches,rather

thanrerunningtheunderlyingcodingagentandevaluator.Consequently,asingleexpensiveonline

discoveryruncansupportmanyinexpensiveevaluationsofalternativeexplorationstrategies.

3.DREAM-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

AsshowninFigure1,DREAM-RSIalternatesbetweenonlineexplorationandoffline“dreaming”to

improveanexecutableexplorationpolicythatallocatesdiscoverycomputation.Duringtheonlinephase,

thepolicyguidesafixeddiscoveryagent,whileafixedevaluatorscorestheresultingcandidatesand

providesdiagnosticfeedback.Theresultingdiscoverytreeservesasareplayworldinwhichalternative

policiescanbeevaluatedusingrecordedoutcomes.AfixedLLM-basedpolicy-developmentagentuses

thisfeedbacktorevisetheexplorationpolicycode,andthebestevaluatedversionisdeployedforthe

nextonlinerollout.Onlytheexploration-policycodechanges;theunderlyingmodels,evaluator,and

executioninterfacesremainfixed.

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

Discoverytreesandtheshareddecisioninterface.Adiscoverytreeisrootedatr,whichrepresents

theinitialworkspacestate.Eachnon-rootnodevhasexactlyoneprimaryparent,eithertheroot

orapreviouslycreatednode.Thisparentidentifieswheretheattemptinvbegins:thediscovery

agentresumestheparent'ssavedworkspaceandusesitsaccumulatedobservationsascontextto

produceanewattempt.Nodevpreservesthisinheritedhistoryandrecordstheoutcomeofthe

newgeneration-evaluationattempt,includingtheresultingfilesystemsnapshot,generatedartifact,

evaluationdiagnostics,andscoresv.Scoresfollowafixedtask-scoringprotocol,withlargervalues

indicatingbetterquality.

Inbothonlineexecutionandofflinereplay,theexplorationpolicyobservesatreeT,initially

containingonlytheroot,andselectsthenodesfromwhichtocontinueexploration.Theeligible

nodesformthesetA(T)={r}U{v∈T:visaleaf},whereleavesaredeterminedfromthecurrently

observedtree.LetW≥1bethenumberofparallelworkers,eachofwhichcanexecuteonegeneration-

evaluationrequestatatime(e.g.concurrentAPIcalls).Theexplorationpolicy'sactionisabatch

C∈A(T;W),whereisthefeasiblebatchset.Eachselectednode

specifiesthestartingpointofoneattempt,sothebatchdeterminesbothwhereexplorationcontinues

andhowmanyattemptsarescheduledinparallel.Boththeonlineandofflinephasesusethissame

decisioninterfacebutdifferinthetransitionthatfollowsaselectedbatch.

Onlinerollout.Lett=1,2,...indextheouteriterations,startingfromaninitialpolicyπ1and

anemptyhistoryHo=().Atiterationt,policyπtguidesanewonlinerolloutwithaccesstothe

completeddiscoveryhistoryH-1.Thishistoryprovidescontextforexplorationbutremainsseparate

fromthenewtreebeingconstructed.Thepolicycodestaysfixedthroughouttherollout.

Letdenotethenewdiscoverytreeafterkcompleteddecisionrounds,with.Therollout

allowsatmostK₁rounds.Atroundk≤K1,theexplorationpolicychoosesanodebatch(

andeachnodeiisassignedtoaworker.Thediscoveryagentusesv'ssavedworkspaceand

availablecontexttoproduceanewcandidate,andtheevaluatorassessestheresult.Theseattempts

runinparallel,eachproducingonenewchildofitsselectedparent.Attachingthecompletedchildren

tothecurrenttreeyields,whileallpreviouslyrecordednodesremainunchanged.Thistransition

isstochasticbecausethediscoveryagentmaygeneratedifferentoutcomesfromthesamestarting

workspace.Forthenextround,thenewlycreatedchildbecomestheselectableleafofanextended

branch,whiletherootremainsselectableforopeningfurtherbranches.Therolloutendswhenthe

policyselectsanemptybatchorcompletesK₁decisionrounds.Aftertherolloutterminates,itsfinal

treeisrecordedasTtandappendedtothehistory,givingH=H-1U{π}.Themethodthenenters

theofflinephaseusingthisexpandedcollectionofreplayworlds.

Offlineevaluation.Duringtheofflinephaseofouteriterationt,thehistoryHremainsfixedwhile

themethodconstructsandevaluatesM≥1policyversions,startingwith

Eachversionisevaluatedseparatelyoneveryhistoricaltreebeforethenextversionis

developedfromtheresultingfeedback.Weusemtoindexpolicyversions,itoindexreplayworlds,and

ktocountdecisionroundswithinonepolicy-worldevaluation.Theouterindextisfixedthroughout

thisphaseandissuppressedinthenotationforreplaytrajectoriesandscores.

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

{r}.Here,denotesthesubtreerevealedafterkcompletedrounds.Thefullrecordedtree

Tiremainsfixed;onlytheportionobservedbythepolicyevolves.Ateachdecision,π"selectsa

batch)usingtherevealedobservations.Unlikeonlineexecution,replayreturns

recordedchildrenoftheselectednodesdeterministicallyratherthangeneratingnewcandidates.

Aftertheexplorationpolicytakesanonemptybatch,thenextobservedtreeis

5

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

)whereChild()denotesthenodesetcontainingunobservedchildren

ofvontreeTigiventhecurrentobservedtree.Forv≠r,Child(v;T,Tm,k)isv'suniquerecorded

child,ifoneexists.Sincevisaleafof,thatchildisstillunrevealed.Forv=r,replayreturnsthe

earliest-createdchildofroutside,openingonepreviouslyunrevealedbranch.Ineithercase,

Child(v;Ti,whennorecordedcontinuationremains.Thenewlyrevealednodesexposetheir

storedobservationsbeforethepolicymakesitsnextdecision.

ReplayallowsatmostK₂decisionroundswhereeachnonemptybatchcountsasoneround,and

terminateswhenthepolicyselects,theroundlimitk=K₂isreached,or,meaning

thatallrecordednodeshavebeenrevealed.Letdenotethenumberofcompleted

roundsattermination,yieldingthefinalsubtree

Thus,replayevaluateshowfartopursueeachopenedbranch,howtogroupattemptsintoparallel

batches,andwhentoopenanotherbranchorstop.Thesedecisionsmaydifferacrosspolicies,but

eachbranchistraversedinitsrecordedparent-childorder,andnooutcomesbeyondTiaregenerated.

Replayobjective.Thereplayobjectivebalancesdiscoveryquality,executioncost,andparallelism.

LetIbethenumberofrevealednon-rootnodes.Althoughreplayitselfdoesnot

executenewdiscoveryattempts,countsthegeneration-evaluationrequestsrepresentedbyits

trajectory.Forfixedcoefficientsβ1,β2≥0,thereplayscoreis

(1)

Thefirsttermmeasuresthebestsolutionqualityattainedduringreplay.Thesecondpenalizesthe

numberofattemptedgenerations.Foranonemptyreplay,thethirdrewardstheaveragenumberof

attemptsexecutedperdecisionround,favoringpoliciesthatbatchusefulcontinuationsratherthan

executethemsequentially.

Policyimprovementandselection.Theevaluationscoreofpolicyversionisitsaveragereplay

scoreacrossthefixedhistory,.Theofflinephasebeginsbyevaluatingthecurrentpolicy

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

andscoresof:,togetherwithfeedbackfromearlierrevisions,toidentifysuccessfuldecisionsand

recurringfailures.Itthenrevisestheexecutablepolicycodetoproduce,whichisevaluatedon

thesametreplayworlds.Replayfeedbackisavailabletothedevelopmentagentbetweenrevisions.

AfterMrevisions,thenextonlinepolicyisselectedfromallMevaluatedversionsas

wherem*∈argmaxme{0.…,M-1}Vm.Becausethecandidatesetincludesthecurrentpolicy,this

selectionsatisfiesvm*≥v⁰.Thus,theselectedpolicyπt+1isnoworsethanthecurrentpolicyπtin

averagereplayscoreonthefixedhistoryH.Theselectedpolicyisthendeployedonlinetocollect

Tt+1,expandingthehistoryavailableforthenextofflineimprovementphase.

4.Experiments

WeevaluateDREAM-RSIacrossthreescientificdiscoverydomains:algorithmengineering,kernel

optimizationandmathoptimization.OurprimarycontrolledbaselineisRecursiveFixedExploration,

6

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

whichusesthesameunderlyingdiscoverysettingandinitializationbutkeepstheexplorationpolicy

fixedacrossrecursivediscoveryrounds.Weadditionallycompareagainsttask-specificdomain

baselines.

Acrossalltasks,DREAM-RSIandRecursiveFixedExplorationusethesamediscoveryagent,

evaluator,initialization,andresourceconstraints.Bothmethodsstartfromthesamemanually

designedexplorationpolicy.Thisexplorationpolicyfollowsasimpleparallelrefiningstrategy:it

launchesmultipleindependentexplorationworkspacesinparallel,witheachworkspacemaintaining

itsownlocaldiscoverytrajectoryandrepeatedlyrefiningitscurrentcandidatebasedonthehistory

accumulatedwithinthatworkspace.Thetwomethodsthereforefollowthesameexplorationpolicy

inthefirstdiscoveryround.Insubsequentrounds,whileRecursiveFixedExplorationkeepsits

explorationpolicystatic,DREAM-RSIprogressivelyrefinesthepolicybydreamingoverareplay

simulatorconditionedonaccumulatedglobaldiscoveryhistory,subsequentlydeployingtheupdated

policyineachnewround.Thediscoverycostisquantifiedbythetotalcumulativenumberof

discovery-agentcalls.

Specifically,weevaluateGemini-3.1ProandGemini-3.7-Flashacrossmultiplerecursivediscovery

roundsviatheGeminiCLI².UnderRecursiveFixedExploration,eachroundforGemini-3.1Pro

executes10parallelworkspaceswithupto11refinementsteps(10×11=110discovery-agent

calls),whereasGemini-3.7-Flashoperates32parallelworkspaceswithupto20refinementsteps

(32×20=640calls).DREAM-RSImaintainsidenticalper-roundbudgets,aligningwiththebaseline

inRound1whileprogressivelyupdatingitspolicyinsubsequentrounds.Furtherdetailsonrecursive

rounds,tasksetups,resourcebudgets,andevaluationprotocolsfollowbelow.

4.1.AlgorithmEngineering

Inthistask,weconsiderLassoRegularizationPathasouralgorithm-engineeringtask,afundamental

computationalprimitiveinhigh-dimensionalstatisticsthatiswidelyusedinmodelselectionand

cross-validationacrossdomainssuchasgenomicsandfinance.Wefollowthebenchmarksettingof

SimpleTES(Yeetal.,2026),wherethegoalistodiscoverefficientimplementationsofthecomplete

Lassoregularizationpathwhilepreservingnumericalcorrectness.Duringdiscovery,weusethesame

17syntheticinstancesasSimpleTES,whichcoverdiverseproblemregimesintermsofdimensionality,

sparsity,featurecorrelation,andactive-setstructure.Toevaluatewhetherthediscoveredalgorithms

generalizebeyondthesearchdistribution,weadditionallyevaluatethemonsixheld-outdownstream

datasetsspanningbothbiologicalandnon-biologicaldomains.

BaselinesandSetup.WecompareagainststandardLassosolverssklearn(Pedregosaetal.,2011)

andglmnet(Friedmanetal.,2010),aswellasSimpleTES(Yeetal.,2026),whichusesGPT-OSS-120B

withareportedbudgetof51,200generations.WeadditionallyincludeRecursiveFixedExplorationas

ourcontrolledbaseline.Specifically,werunbothRecursiveFixedExplorationandDREAM-RSIfor5

rounds.

MainResults.Figure3(a)summarizestheLassodiscoveryresults.Acrossbothdiscovery-agent

backbones,DREAM-RSIachievesabetterdownstreamquality-computetrade-offthanRecursiveFixed

Exploration.WithGemini-3.1Pro,itreducestheaverageruntimeacrossthesixheld-outdatasetsfrom

3587.1msto2931.0mswhileusingonly317discovery-agentcalls,comparedwith550callsforfixed

exploration.WithGemini-3.7-Flash,DREAM-RSIfurtherreducestheaverageruntimefrom2516.7ms

to2350.6msusing1879callsinsteadof3200.Despiteusingsubstantiallylessdiscoverycompute,

²/

7

Dream-RSI:RecursiveSelf-ImprovementthroughEvolvingWorlds

Non-biologicalBiological

MethodModelComputeAvg.

GisetteRCV1DNALeukemiaColonDukeBreast

Previoussolvers

sklearn11275.2252881.793.8227.2229.8374.044180.3

glmnet-9063.673072.8351.945.024.247.713767.5

SimpleTESgpt-oss-120b51,2003141.919625.615.915.511.618.13804.8

SimpleTES÷gpt-oss-120b51,2008651.041143.137.628.219.531.18318.4

OurSystem

Gemini-3.1-Pro5501861.819550.141.526.114.528.43587.1

RecursiveFixedExploration

Gemini-3.7-Flash32001133.113873.029.824.115.724.42516.7

Gemini-3.1-Pro3172841.014616.049.930.216.432.52931.0

DREAM-RSI

Gemini-3.7-Flash18791091.912923.431.421.012.223.62350.6

(a)Finalperformance.

Gemini-3.1-ProGemini-3.7-Flash

100300

Disc