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YaoqiYe*1 YiranZhao*2 KeyuDuan1 ZeyuZheng3 KenjiKawaguchi1 CihangXie4 MichaelQizheShieh1
| | | | Abstract | | | | 1.Introduction | | | | | | | |
| --- | --- | --- | -------- | --- | --- | --- | --------------- | --- | -------- | -------- | --- | ------ | ------ | ---- |
| | | | | | | | Recent advances | | in large | language | | models | (LLMs) | (Guo |
etal.,2025;Yangetal.,2025;Seedetal.,2025;Teametal.,
| While | large | language | | models | (LLMs) | exhibit | | | | | | | | |
| ----- | ----- | -------- | --- | ------ | ------ | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
2025)haveshowntheireffectivenessinaddressingawide
strongreasoningabilities,theirperformanceon
6202 raM 9 ]IA.sc[ 1v86080.3062:viXra
complextasksisoftenconstrainedbythelimita- range of complex tasks (Wang et al., 2024a; Hsiao et al.,
| | | | | | | | 2025;Shietal.,2025;Quetal.,2025). | | | | | Nevertheless,akey | | |
| ------------------------------ | --- | ----------- | --- | -------------- | ----------- | ---------- | --------------------------------- | -------- | ------ | ------------ | ---- | ----------------- | ------------ | --- |
| tionsoftheirinternalknowledge. | | | | | Acompelling | | | | | | | | | |
| | | | | | | | limitation | remains: | these | models | rely | on | a fixed body | of |
| approach | | to overcome | | this challenge | | is to aug- | | | | | | | | |
| | | | | | | | knowledge | acquired | during | pretraining, | | which | inherently | |
mentthesemodelswithexternaltools—suchas
| | | | | | | | restricts their | ability | to | adapt | to new | or time-sensitive | | in- |
| ------ | ------------ | --- | --- | ------------ | --- | -------- | --------------- | ------- | --- | ----- | ------ | ----------------- | --- | --- |
| Python | interpreters | | for | mathematical | | computa- | | | | | | | | |
formation(Gaoetal.,2023;Zhuetal.,2025;Wangetal.,
tionsorsearchenginesforretrievingfactualin-
| | | | | | | | 2024b; Cheng | et | al., 2024; | Matarazzo | | & | Torlone, | 2025). |
| ---------- | --- | -------- | --- | -------- | ------ | ------ | ------------ | --- | ---------- | --------- | --- | --- | -------- | ------ |
| formation. | | However, | | enabling | models | to use | | | | | | | | |
Tomitigatethisissueandenhancemodelflexibility,recent
thesetoolseffectivelyremainsasignificantchal-
researchhasfocusedonenablingLLMstointeractwithex-
| lenge. | Existing | methods | | typically | rely | on cold- | | | | | | | | |
| ------ | --------- | ------- | ----- | --------- | ---------- | -------- | --------------------------- | --- | --- | ------------------------- | --- | --- | --- | --- |
| | | | | | | | ternaltoolsduringinference. | | | Thisincludesgeneratingand | | | | |
| start | pipelines | that | begin | with | supervised | fine- | | | | | | | | |
executingPythoncodeformathematicalreasoning,lever-
tuning(SFT),followedbyreinforcementlearning
agingwebsearchenginestoaccessup-to-dateanddomain-
(RL).Theseapproachesoftenrequiresubstantial
specificcontent,andinvokingdedicatedhelpermodelsfor
| amounts | of | labeled | data | for SFT, | which | is ex- | | | | | | | | |
| ------- | --- | ------- | ---- | -------- | ----- | ------ | --- | --- | --- | --- | --- | --- | --- | --- |
pensivetoannotateorsynthesize. Inthiswork, specializedsubtasks(Guoetal.,2024;Teametal.,2025;Li
etal.,2025b;Jinetal.,2025a;Fengetal.,2025).
weproposeIn-ContextReinforcementLearning
| (ICRL), | an | RL-only | framework | | that | eliminates | | | | | | | | |
| ------- | --- | ------- | --------- | --- | ---- | ---------- | ------------ | -------- | --- | --------- | --- | ---- | ------ | ------ |
| | | | | | | | The dominant | training | | paradigms | for | LLMs | either | lever- |
theneedforSFTbyleveragingfew-shotprompt- agereinforcementlearning(RL)withverifiablerewardsig-
ing during the rollout stage of RL. Specifically, nals(Guoetal.,2025;Jinetal.,2025a;Zhaoetal.,2025),
ICRLintroducesin-contextexampleswithinthe
| | | | | | | | or adopt | a cold-start | strategy | | that begins | with | supervised | |
| --- | --- | --- | --- | --- | --- | --- | -------- | ------------ | -------- | --- | ----------- | ---- | ---------- | --- |
rollout prompts to teach the model how to in- fine-tuning(SFT)followedbyanRLphase(Meietal.,2025;
| voke | external | tools. | Furthermore, | | as | training | | | | | | | | |
| ---- | -------- | ------ | ------------ | --- | --- | -------- | ------------------ | --- | ----------------------------- | --- | --- | --- | --- | --- |
| | | | | | | | Nguyenetal.,2025). | | DirectlyapplyingRLfromscratch | | | | | |
progresses, the number of in-context examples often yields poor performance, as the model lacks initial
isgraduallyreduced,eventuallyreachingazero- tool-useabilitiesandstruggleswithineffectiveexploration.
shotsettingwherethemodellearnstocalltools
WhileincorporatinganSFTstagecanguidethemodelto-
independently.Weconductextensiveexperiments wardamorefavorableinitialization,ittypicallyrequiresa
acrossarangeofreasoningandtool-usebench- largeamountofhigh-qualitylabeleddata,whichisexpen-
ICRL
marks. Results show that achieves state- sivetoannotateorsynthesize.
| of-the-art | | performance, | | demonstrating | | its effec- | | | | | | | | |
| ---------- | --- | ------------ | --- | ------------- | --- | ---------- | ------------- | --- | ------------ | --- | ---------- | --- | ------------- | --- |
| | | | | | | | In this work, | | we introduce | | In-Context | | Reinforcement | |
tivenessasascalable,data-efficientalternativeto
| | | | | | 1 | | Learning | (ICRL), | a lightweight | | and | supervision-efficient | | |
| --- | --- | --- | --- | --- | --- | --- | -------- | ------- | ------------- | --- | --- | --------------------- | --- | --- |
traditionalSFT-basedpipelines.
frameworkfortrainingLLMstoperformtool-augmented
| | | | | | | | reasoning. | UnlikepriorapproachesthatrelyonSFT,ICRL | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | ---------- | --------------------------------------- | --- | --- | --- | --- | --- | --- |
teachestoolusedirectlythroughRLrolloutsthatareaug-
*Equal contribution 1National University of Singapore mentedwithin-contextdemonstrations. Specifically,during
| 2Salesforce | AI | Research | 3University | | of California | Berkeley | | | | | | | | |
| ----------- | --- | -------- | ----------- | --- | ------------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
RLtraining,weconstructeachrolloutpromptbyprepending
| 4University | of California, | | Santa | Cruz. | Correspondence | to: Yi- | | | | | | | | |
| ----------- | -------------- | --- | ----- | ----- | -------------- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
asmallnumberoffew-shotexamplesthatillustratehowto
| ran Zhao | <zhaoyiran0924@gmail.com>, | | | | Michael | Qizhe Shieh | | | | | | | | |
| -------- | -------------------------- | --- | --- | --- | ------- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- |
<michaelshieh@comp.nus.edu.sg>. reasonstep-by-step,invoketoolsinastructuredformat,and
| | | | | | | | generatefinalanswers. | | Thesedemonstrationsserveassoft | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --------------------- | --- | ------------------------------ | --- | --- | --- | --- | --- |
Preprint.March10,2026. supervisionduringexploration,guidingthemodeltoward
| 1Code | | | | https://github.com/ | | | | | | | | | | |
| ----- | --- | --- | --- | ------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
is publicly available at successfulbehaviorwithoutrequiringlabeledtrajectories.
applese233/ICRL
1
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
Figure1.ICRLtrainingworkflow.Themodelistrainedthroughamulti-stagecurriculumthatgraduallyreducesthenumberofin-context
examplesintherollouttemplate.Ateachstage,theLLMgeneratestool-augmentedrollouts,receivesrewards,andupdatesitspolicyvia
reinforcementlearning,enablingatransitionfromimitationtoautonomoustooluse.
Furthermore,astrainingprogresses,wegraduallyreducethe TheseresultshighlightICRL’sabilitytogeneralizetodi-
numberofdemonstrationsincludedintheserolloutprompts, versetool-augmentedreasoningdomainsanditspotentialas
transitioningthemodelfromfew-shottozero-shotsettings. aunified,scalableframeworkfortrainingtool-usingmodels
Thisprogressivereductionformsacurriculumthatencour- withoutcostlysupervision.
agesthemodeltointernalizetool-usestrategiesandproduce
structuredoutputsautonomously,withoutrelyingonprompt- 2.In-ContextReinforcementLearning(ICRL)
| basedscaffolding. | WeoptimizethemodelusingRLwitha | | | | | | | | |
| ----------------- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- |
rewardthatbalancestaskaccuracyandformatcorrectness. In this section, we formally introduce tool use in LLMs
Toensurestability,weadoptGRPO(Shaoetal.,2024)with anddescribehowRLcanbeappliedtotrainsuchbehavior.
lossmaskingtoignorenon-trainabletooloutputs. Byem- WealsopresenttheoverallworkflowofICRL,detailingits
beddingandgraduallyremovingdemonstrationsfromthe trainingtemplates,learningprocess,andrewarddesign.
RLrollouts,ICRLmergestheefficiencyofpromptingwith
theadaptabilityofRL,offeringascalable,supervision-light
2.1.ToolUseinLLMs
alternativetotraditionalSFT+RLpipelines.
| | | | | When | LLMs encounter | queries | that exceed | the | scope of |
| --- | --- | --- | --- | ---- | -------------- | ------- | ----------- | --- | -------- |
Weconductcomprehensiveexperimentsacrossarangeof
theirinternalknowledge,theymustleverageexternaltools
QA and reasoning benchmarks to evaluate the effective- to obtain updated information or perform more complex
nessofICRL.Withoutrelyingonsupervisedfine-tuningor
| | | | | reasoning. | Forexample,searchenginescanprovideaccess | | | | |
| --- | --- | --- | --- | ---------- | ---------------------------------------- | --- | --- | --- | --- |
ground-truthtooltraces,ICRLachievesstate-of-the-artper- torecentknowledge,whilePythoninterpreterscanbeused
formanceonchallengingQAdatasets,outperformingstrong toexecutestructuredreasoningprocedures.
| baselines such | as ZeroSearch | (Sun et al., | 2025), Search- | | | | | | |
| -------------- | ------------- | ------------ | -------------- | --- | --- | --- | --- | --- | --- |
Formally,givenaqueryqandanexternaltoolT,themodel
R1(Jinetal.,2025a),andParallelSearch(Zhaoetal.,2025)
| | | | | generates | a response | y = | (y ,y ,...,y | ), where | each |
| ---------------------------------------------- | --- | --- | --- | --------- | -------------- | -------- | ------------ | --------- | -------- |
| byupto8.9onQwen2.5-3B(Yangetal.,2024b)and7.3on | | | | | | | 1 2 | |y| | |
| | | | | token | is conditioned | not only | on the | query and | previous |
Qwen2.5-7Binaverageexactmatchaccuracy.Thegainsare
tokens,butalsoonahistoryofpriorinteractionswiththe
especiallypronouncedonmulti-hopreasoningtasks,where
ICRLachievesdouble-digitimprovementsondatasetslike tool. Thisdefinesaconditionaldistributionoftheform:
| TriviaQA(Joshietal.,2017),2Wiki(Hoetal.,2020),and | | | | | | |y| | | | |
| ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Musique(Trivedietal.,2022). Furthermore,incontrastto (cid:89)
| | | | | | π θ (y |q,T)= | | π θ (y t |y | <t ,q,H t | ) (1) |
| --- | --- | --- | --- | --- | ------------- | --- | ----------- | --------- | ----- |
methodssuchasO2-Searcher(Meietal.,2025)thatrequire
t=1
| cold-start SFT | to learn complex | tool-use | behavior, ICRL | | | | | | |
| -------------- | ---------------- | -------- | -------------- | --- | --- | --- | --- | --- | --- |
learnssuchcapabilitiesdirectlythroughin-contextexamples Here,π θ isthemodelparameterizedbyθ,andH t denotes
| | | | | the sequence | of previous | actions | taken | by the | model and |
| --- | --- | --- | --- | ------------ | ----------- | ------- | ----- | ------ | --------- |
duringRLrollouts—demonstratingsuperiordataefficiency.
thecorrespondingobservationsreturnedbythetoolupto
BeyondwebQA,wealsoevaluateICRLonmathreasoning
| | | | | step t. | Specifically, | the interaction | | between | the model |
| ----------------------------------- | ----------- | ------------- | ---------- | ------- | ------------------ | --------------- | ---------- | ----------- | --------- |
| tasksinvolvingcodeexecutionasatool. | | OntheAIME2024 | | | | | | | |
| | | | | and the | tool is structured | as | a sequence | of actions. | At |
| and AIME2025 | benchmarks, | ICRL matches | or exceeds | | | | | | |
the performance of ReTool (Feng et al., 2025), a strong each time step, the model may choose to (i) perform in-
ternalreasoning,(ii)issueaquerytotheexternaltool,or
SFT+RLbaseline,despiteusingnosupervisedpretraining.
| | | | | (iii) return | a final | answer. | These actions | are | embedded |
| --- | --- | --- | --- | ------------ | ------- | ------- | ------------- | --- | -------- |
2
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
| inthegeneratedtextinastructuredformat,suchasXML | | | | | | | where | | | | | | |
| ----------------------------------------------- | ----- | ----------- | --------- | ----- | ---- | ------------ | ---------------------- | --- | --- | ----------------- | --- | --- | --- |
| tags, | which | distinguish | reasoning | steps | from | tool invoca- | | | | | | | |
| | | | | | | | R(τi)−mean({R(τi)|τi∼π | | | (τ),i=1,2,...,N}) | | | |
θold
tions and answers. For example, a reasoning step might Ai= , (4)
| | | | | | | | | std({R(τi)|τi∼π | | θold (τ),i=1,2,...,N}) | | | |
| --- | ------- | ---------------------- | --- | --- | -------- | ----- | --- | --------------- | --- | ---------------------- | --- | --- | --- |
| be | denoted | as <think>...</think>, | | | a search | query | | | | | | | |
as <search>...</search>, a retrieved information andr (θ)=π (τ |q,τ )/π (τ |q,τ ).
| | | | | | | | i,t | | θ i,t | i,<t θold | i,t i,<t | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ----- | --------- | -------- | --- | --- |
as<information>...</information>,andafinal
| answeras<answer>...</answer>. | | | | | | | 2.3.ICRL | | | | | | |
| ----------------------------- | --- | --- | --- | --- | --- | --- | -------- | --- | --- | --- | --- | --- | --- |
Furthermore,thetoolfunctionsasaresponsemechanism. Training Process. Rather than training models from
Forexample,asearchenginecanbemodeledasaretrieval
scratchusingreinforcementlearning,whichoftensuffers
| | | V∗ | V∗, | V∗ | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
function T : → where is the space of textual fromsparserewardsandinefficientexploration,orrelying
sequences. Givenasearchqueryq′,thetoolreturnsanob- exclusivelyonfew-shotprompting,whichincurssubstantial
servationo=T(q′),suchasthetop-kdocumentsretrieved
inferenceoverhead,weintroduceICRL,aframeworkthat
fromacorpus. Thisobservationisappendedtothemodel’s integrates the strengths of both approaches. ICRL lever-
contextandusedinsubsequentgenerationsteps.
agesthesampleefficiencyandinductivebiasoffew-shot
promptingwhilebenefitingfromtheexplorationcapabilities
| 2.2.RLwithToolUse | | | | | | | ofreinforcementlearning. | | | | | | |
| ----------------- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | --- | --- | --- |
RL Objective Function. After formulating the tool- Atthebeginningoftraining,weincorporateasmallnumber
augmentedreasoninginLLMsasaMarkovDecisionPro- of tool-use demonstrations into the model’s rollout tem-
cess(MDP),wecandefinethecorrespondingreinforcement plate. These examples guide the model toward effective
learning(RL)objectiveasfollows: tool-augmentedreasoningviain-contextlearning,akinto
| | | | | | | | few-shotprompting. | | Theresultingpolicyisdenotedas: | | | | |
| --- | --- | --------------- | --- | ------ | --- | --- | ------------------ | --- | ------------------------------ | --- | --- | --- | --- |
| | max | E | [r | (q,y)] | | | | | | | | | |
| | | q∼D,y∼πθ(·|q,T) | | ϕ | | | | | | | | | |
πθ
| | | | | | | (2) | | | | |y| | | | |
| --- | --- | --- | ------------- | --- | ---------- | --- | ---- | --- | ------ | -------- | ------- | --- | --- |
| | | −βD | [π (y |q,T)∥π | | (y |q,T)], | | | | | (cid:89) | | | |
| | | KL | θ | ref | | | π (y | |P | ,q,T)= | π (y |P | ,y ,q,H | ), | (5) |
| | | | | | | | θ | N | | θ t | N <t | t | |
t=1
| whereπ | | isthepolicyLLM,π | | isthereferenceLLM,r | | | | | | | | | |
| ------ | --- | ---------------- | --- | ------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | θ | | | ref | | ϕ | | | | | | | |
istherewardfunctionandD isKL-divergencemeasure. whereP N representsthefew-shotpromptconsistingofN
KL
| | | | | | | | demonstrationexamples. | | | Table1showsaconcreteexample | | | |
| --- | --- | --- | --- | --- | --- | --- | ---------------------- | --- | --- | --------------------------- | --- | --- | --- |
ofrollouttemplate.
| LossMasking. | | UnliketraditionalRL,whichoptimizes | | | | | | | | | | | |
| ------------ | --- | ---------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
solely over model-generated tokens, tool-augmented rea- Aftertrainingforseveralsteps,themodelbeginstoacquire
soning introduces retrieved content into the rollout se- tool-usecapabilitieswiththeguidanceoftheinitialfew-shot
quence—tokens that are not produced by the model and promptP . Oncesufficientlearningprogressisobserved,
N
therefore do not reflect its internal reasoning or decision- wepausetrainingandreducethenumberofdemonstration
| makingprocess. | | Toaddressthis,weadoptalossmasking | | | | | | | | | | | |
| -------------- | --- | --------------------------------- | --- | --- | --- | --- | -------------------- | --- | --- | ----------------------------- | --- | --- | --- |
| | | | | | | | examplesintheprompt. | | | Theupdatedpolicyconditionedon | | | |
strategytailoredforRLwithtooluse, whichexcludesre- areducedpromptP isdefinedas:
N−1
| trieved | content | from | the optimization. | | Specifically, | only | | | | | | | |
| ------------------------------------------------ | ------- | ---- | ----------------- | --- | ------------- | ---- | --- | --- | --- | --- | --- | --- | --- |
| tokensgeneratedbythelanguagemodelcontributetothe | | | | | | | | | | |y| | | | |
(cid:89)
| | | | | | | | π (y |P | | ,q,T)= | π (y |P | ,y | ,q,H | ), (6) |
| --- | --- | --- | --- | --- | --- | --- | ------- | --- | ------ | ------- | --- | ---- | ------ |
policygradient,whileretrievedspansaremaskedoutand θ N−1 θ t N−1 <t t
| excludedfromthelosscomputation. | | | | Thistargetedoptimiza- | | | | | | t=1 | | | |
| ------------------------------- | --- | --- | --- | --------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
tionensuresthatlearningremainsfocusedonthemodel’s whereP denotesapromptwithN −1demonstration
N−1
ownbehavior—suchastoolusage,intermediatereasoning,
| | | | | | | | examples. | Thisprocessisrepeatediteratively,progressively | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --------- | ---------------------------------------------- | --- | --- | --- | --- | --- |
andfinalanswers—withoutbeingaffectedbyfixed,untrain-
reducingthenumberofdemonstrations,untilnoexamples
| ablecontentfromexternalsources. | | | | | | | remainintheprompt. | | | | | | |
| ------------------------------- | --- | --- | --- | --- | --- | --- | ------------------ | --- | --- | --- | --- | --- | --- |
GRPOwithToolUse. WeadoptGRPO(Shaoetal.,2024) RewardDesign. Wedesignacompositerewardfunction
| totrainπ | | ontheRLdatasetD | | ={q ,q | ,··· ,q | }. Specif- | | | | | | | |
| -------- | --- | --------------- | --- | ------ | ------- | ---------- | ------------------------------------------------- | --- | --- | --- | --- | --- | --- |
| | θ | | | 1 | 2 | n | thatcombinestheansweraccuracyandformatcorrectness | | | | | | |
ically,forq ∈D,weusetheoldpolicyfrompreviousstep toprovidearicherlearningsignal:
| π | tosampleagroupofN | | individualresponsesτ | | | . Then, | | | | | | | |
| --------------------- | ----------------- | --- | -------------------- | --- | -------- | -------- | ---------------- | ------ | -------------- | ------------- | ------- | ------------ | ----- |
| θold | | | | | | i | | | | | | | |
| theRLlossisdefinedas: | | | | | | | r (q,y)=α·reward | | | +(1−α)·reward | | | , (7) |
| | | | | | | | ϕ | | | acc | | format | |
| | | | | | | |τi| | where α | is the | hyperparameter | to | balance | two rewards. | |
| | | | | 1 | N | | | | | | | | |
| | (θ)=E | | | | (cid:88) | (cid:88) | | | | | | | |
L GRPO Specifically, the accuracy-based reward is computed us-
| | | τi∼πθold | (q),q∼DRL(cid:80)N | | |τ | | (3) | | | | | | | |
| --- | --- | -------- | ------------------ | --- | ---- | --- | --- | --- | --- | --- | --- | --- | --- |
i=1 i i=1 t=1 ing exact match (EM) between the model’s predicted an-
| | | | | ,ϵ)−β·D | | | swer and | the | ground | truth. The reward | is | assigned | as |
| --- | --- | ------ | --------- | ------- | ----- | ----------- | -------- | --- | ------ | ----------------- | --- | -------- | --- |
| | | CLIP(r | i,t (θ),A | i | KL [π | θ ∥π ref ], | | | | | | | |
3
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
Table1.Few-shotrollouttemplateinICRL.
Few-ShotPromptTemplate
Solvethefollowingproblemstepbystep. Youmustconductreasoninginside<think>...</think>everytimeyougetnew
information.Afterreasoning,ifyoufindyoulacksomeknowledge,youcancallasearchengineby<search>query</search>
anditwillreturnresultsbetween<information>...</information>.Youcansearchasmanytimesasyouwant.Finally,
providetheanswerinside<answer>...</answer>.
Herearesomeexamples:
ExampleProblem:q
demo
ExampleSolution:<think>...</think><search>...</search>
<information>...</information><think>...</think><answer>a</answer>
| (repeatedforN | | examples | | | | | | | | | | |
| ------------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Nowsolvethefollowingproblem:
ActualProblem:question
| Table2.Formatviolationpenaltiesforcomputingreward | | | | | . | | | | | | | |
| ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
format
Algorithm1ICRL
| Violation | | | Rationale | | | | | | | | | |
| --------- | --- | --- | --------- | --- | --- | ------ | ------- | -------- | ----------- | ----- | --- | ------ |
| | | | | | | Input: | Initial | policy π | , reference | model | π | , tool |
| | | | | | | | | θ | | | ref | |
No<answer>tag Mustprovidestructuredanswer T, initial few-shot prompt P , dataset partitions
N
Unbalanced<answer>tags ProperXMLstructurerequired {D(N),D(N−1),...,D(0)}, reward function r (·),
ϕ
| No<think>tag | | | Shoulddemonstratereasoning | | | | | | | | | |
| --------------------- | --- | --- | -------------------------- | --- | --- | ---------------- | --- | --- | --- | --- | --- | --- |
| Unbalanced<think>tags | | | | | | numberofRLstepsT | | | | | | |
ProperXMLstructurerequired
No<search>usage Shouldutilizeavailabletool Output: Trainedmodelπ θ
| Emptyanswercontent | | | Answermustbesubstantive | | | 1: fork=N | | to0do | | | | |
| ------------------ | --- | --- | ----------------------- | --- | --- | --------- | ------- | ------------ | --- | ------ | ---- | --- |
| | | | | | | 2: | // Step | 1: Construct | | prompt | with | |
k demonstrations
P ←selectkexamplesfromP
| reward | = 1 if the | prediction | exactly | matches | the cor- | 3: | k | | | N | | |
| ------------------------- | ---------- | ---------- | ------- | ------- | -------- | --- | ------------------------------------------ | --- | --- | --- | --- | --- |
| | acc | | | | | | D(k)←RLtrainingsubsetforcurrentpromptlevel | | | | | |
| rectanswer,and0otherwise. | | | | | | 4: | | | | | | |
| | | | | | | 5: | fort=1toT | do | | | | |
The reward component evaluates the model’s adher- 6: forq∈D(k)do
format
| encetotheexpectedstructuredoutputformat,specifically | | | | | | 7: | π | ←π | | | | |
| ---------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
θold θ
| | | | | | | 8: | Sample | N trajectories | | {τ | ,...,τ | } ∼ |
| ----------------------- | --- | --- | -------------- | --- | --- | --- | ------ | -------------- | --- | --- | ------ | --- |
| thecorrectuseofXMLtags. | | | Itisdefinedas: | | | | | | | 1 | N | |
π (q,P ,T)
θold k
| | | | (cid:88) | | | | foreachtrajectoryτ | | do | | | |
| --- | ------ | ----- | ----------- | --- | --- | --- | ------------------ | --- | --- | --- | --- | --- |
| | reward | =1.0− | penalty(v), | | (8) | 9: | | | i | | | |
format
| | | | | | | 10: | | Computereward:r | ϕ | (q,τ i ) | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --------------- | --- | -------- | --- | --- |
v∈V
| | | | | | | 11: | | ComputenormalizedadvantageA | | | i | |
| --- | --- | --- | --- | --- | --- | --- | --- | --------------------------- | --- | --- | --- | --- |
whereV denotesthesetofformatviolationsidentifiedinthe 12: Computeimportanceweightsr (θ)
i,t
| | | | | | | 13: | endfor | | | | | |
| ---------------- | --- | ----------------------------------- | --- | --- | --- | --- | -------------- | --- | --- | ---- | --- | --- |
| model’sresponse. | | Thepenaltyfunctionpenalty(v)assigns | | | | | | | | | | |
| | | | | | | 14: | Updatepolicy:π | | ←π | −∇ L | | |
apredefinedcosttoeachviolation,asspecifiedinTable2. θ θ θ GRPO
endfor
15:
| Withtheproposedrewarddesign,weoptimizethepolicy | | | | | | 16: | endfor | | | | | |
| ----------------------------------------------- | --- | --- | --- | ----------- | --- | ---------- | ------ | --- | --- | --- | --- | --- |
| usingtheRLobjectivedefinedinEquation3. | | | | Thecomplete | | 17: endfor | | | | | | |
trainingprocedureforICRLisoutlinedinAlgorithm1.
| | | | | | | convergenceduringRLtraining. | | | Forimprovedtrainingeffi- | | | |
| --- | --- | --- | --- | --- | --- | ---------------------------- | --- | --- | ------------------------ | --- | --- | --- |
3.Experiment ciency,allmodelsareloadedusingbfloat16precision.
3.1.Setup
WeevaluatetheeffectivenessofICRLbycom-
Baselines.
Backbone Models. We apply ICRL to the Qwen2.5 paringitagainstseveralstate-of-the-artmethodsfortraining
model family (Yang et al., 2024a), focusing primarily on tool-augmentedLLMs. Thesebaselinesfallintothreemain
Qwen2.5-3B-InstructandQwen2.5-7B-Instruct,andfurther categories. Directpromptingmethodsincludemodelsthat
evaluatingonQwen2.5-14B-Instruct. Wealsoextendour performinferenceusingdirectinputsorpromptingstrate-
experimentstotheQwen3series(Yangetal.,2025),particu- giessuchasChain-of-Thought(CoT)reasoning(Weietal.,
larlyQwen3-8B,whichincorporatesRLenhancements. All 2022). Retrieval-basedmethodsleverageexternalinfor-
theseinstruction-tunedmodelsarewidelyadoptedforques- mationthroughtechniqueslikeRetrieval-AugmentedGen-
tionansweringandreasoningtasks. Wechoosetheinstruct eration(RAG),includingstandardRAG(Lewisetal.,2020),
variants over base models due to their strong instruction- InterleavingRetrievalChain-of-Thought(IRCoT)(Trivedi
followingcapabilities,whichenablefasterandmorestable etal.,2023),andSearch-o1(Lietal.,2025a). Fine-tuning-
4
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
Table3.MainResultsofICRL:ExactMatch(EM)Accuracy(%)onvariousdifficultQAdatasets.Thebestperformanceissetbold.The
secondbestperformanceisunderlined.
DifficultQuestionAnswering
| | | Model | Method | | | | | | | | Average | |
| --- | --- | ----- | --------- | --- | -------- | -------- | ----- | ------- | --------- | ---- | ------- | --- |
| | | | | | TriviaQA | HotpotQA | 2Wiki | Musique | Bamboogle | | | |
| | | | Direct | | 28.8 | 14.9 | 24.4 | 2.0 | | 2.4 | 14.50 | |
| | | | CoT | | 3.2 | 2.1 | 2.1 | 0.2 | | 0.0 | 1.52 | |
| | | | IRCoT | | 31.2 | 16.4 | 17.1 | 6.7 | | 24.0 | 19.08 | |
| | | | Search-o1 | | 47.2 | 22.1 | 21.8 | 5.4 | | 32.0 | 25.70 | |
| | | | RAG | | 54.4 | 25.5 | 22.6 | 4.7 | | 8.0 | 23.04 | |
| | | | SFT | | 29.2 | 18.6 | 24.8 | 4.4 | | 11.2 | 17.64 | |
Qwen2.5-3B
| | | | R1-instruct | | 44.9 | 20.8 | 27.5 | 6.0 | | 19.2 | 23.68 | |
| --- | ---------- | --- | -------------- | --- | ---- | ---- | ---- | ---- | --- | ---- | ---------- | --- |
| | | | RejectSampling | | 48.8 | 24.0 | 23.3 | 5.9 | | 21.0 | 24.60 | |
| | | | Search-R1 | | 54.5 | 32.4 | 31.9 | 10.3 | | 26.4 | 31.10 | |
| | | | ZeroSearch | | 57.4 | 27.4 | 30.0 | 9.8 | | 11.1 | 27.14 | |
| | | | ICRL | | 72.6 | 35.4 | 39.2 | 20.0 | | 33.6 | 40.16+8.94 | |
| | | | Direct | | 40.8 | 18.3 | 25.0 | 3.1 | | 12.0 | 19.84 | |
| | | | CoT | | 18.5 | 9.2 | 11.1 | 2.2 | | 23.2 | 12.84 | |
| | | | IRCoT | | 47.8 | 13.3 | 14.9 | 7.2 | | 22.4 | 21.12 | |
| | | | Search-o1 | | 44.3 | 18.7 | 17.6 | 5.8 | | 29.6 | 23.20 | |
| | | | RAG | | 58.5 | 29.9 | 23.5 | 5.8 | | 20.8 | 27.70 | |
| | | | SFT | | 35.4 | 21.7 | 25.9 | 6.6 | | 11.2 | 20.16 | |
| | Qwen2.5-7B | | R1-base | | 53.9 | 24.2 | 27.3 | 8.3 | | 29.6 | 28.66 | |
| | | | R1-instruct | | 53.7 | 23.7 | 29.2 | 7.2 | | 29.3 | 28.62 | |
| | | | RejectSampling | | 59.2 | 33.1 | 29.6 | 12.3 | | 35.5 | 33.94 | |
| | | | Search-R1 | | 61.0 | 37.0 | 41.4 | 14.6 | | 36.8 | 38.16 | |
| | | | ZeroSearch | | 65.2 | 34.6 | 35.2 | 18.4 | | 27.8 | 36.24 | |
| | | | ParallelSearch | | 62.8 | 42.9 | 42.4 | 19.7 | | 41.1 | 41.78 | |
| | | | ICRL | | 75.4 | 42.6 | 53.6 | 26.0 | | 48.0 | 49.12+7.34 | |
based methods involve approaches such as SFT (Chung EvaluationBenchmarks. WeevaluateICRLandvarious
et al., 2024), RL without search (R1) (Guo et al., 2025), baselinesonseveralwidely-usedQAbenchmarks,including
andRejectionSampling(Ahnetal.,2024). Wealsoinclude TriviaQA(Joshietal.,2017),HotpotQA(Yangetal.,2018),
recentRLmethodsthatintegratesearchcapabilities,suchas 2Wiki (Ho et al., 2020), Musique (Trivedi et al., 2022),
Search-R1(Jinetal.,2025a),ZeroSearch(Sunetal.,2025), andBamboogle(Pressetal.,2023). Sinceourmodelsare
O2-Searcher(Meietal.,2025),andParallelSearch(Zhao
trainedontheNaturalQuestions(NQ)dataset,weexclude
et al., 2025). These baselines provide a comprehensive NQfromtheevaluationtoavoiddataleakage. Thesebench-
comparisontovalidatethegeneralityandadvantagesofour markscoverdiversedomainsandreasoningtypes,providing
proposedICRLframework. acomprehensiveassessmentofmodelperformance.Further-
more,toensureevaluationefficiency,werandomlysample
| Training | Datasets. | | We use | the Natural | Questions (NQ) | | | | | | | |
| -------- | --------- | --- | ------ | ----------- | -------------- | --- | -------------------------------- | --- | --- | --- | ----------------- | --- |
| | | | | | | | upto500questionsfromeachdataset. | | | | Theselectedbench- | |
dataset(Kwiatkowskietal.,2019)astheprimarytraining
marksincludebothin-domaingeneralQAtasks(e.g.,Triv-
| corpus. | The | dataset | is loaded | via FlashRAG | (Jin et | al., | | | | | | |
| ------- | --- | ------- | --------- | ------------ | ------- | ---- | --- | --- | --- | --- | --- | --- |
iaQA,HotpotQA)andout-of-domainmulti-hopQAtasks
2025b),whichprovidespreprocessedquestion-answerpairs
(e.g.,2Wiki,Musique,andBamboogle),allowingustothor-
| withgold-standardanswers. | | | | NQcontainsrealuserqueries | | | | | | | | |
| ------------------------- | --- | --- | --- | ------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
oughlytestthegeneralizationandreasoningcapabilitiesof
fromGoogleSearch,eachpairedwithWikipediapassages
differentmethods.
| that | includethe | correct | answer. | To support | ourproposed | | | | | | | |
| ---- | ---------- | ------- | ------- | ---------- | ----------- | --- | --- | --- | --- | --- | --- | --- |
trainingmethod,werandomlysampledthreequestionsfrom
thewebandusedGPT-5.22togeneratefew-shotexamples Reward. The hyperparameter α in Equation 7 is set to
formattedaccordingtotherollouttemplateshowninTable1. 0.8. Furthermore,fortheformatviolationpenaltiesinTa-
Tosimulatereal-worldtool-usebehavior,weintegratethe ble2,theweightsaresetto0.5,0.2,0.15,0.1,0.1,and0.2
| SerperAPI3 | | | | | | | respectivelyfromtoptobottom. | | | | | |
| ---------- | --- | --- | --- | --- | --- | --- | ---------------------------- | --- | --- | --- | --- | --- |
acrossallmodelstoretrieveliveresultsfrom
| theGoogleSearchengine. | | | Forfairness,eachqueryretrieves | | | | | | | | | |
| ---------------------- | --- | --- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- |
thetop3documentsrequiredforsearch-basedreasoning. ImplementationDetails. Weimplementourmethodus-
| | | | | | | | ing the Volcano | Engine | Reinforcement | | Learning | (VeRL) |
| --- | --- | --- | --- | --- | --- | --- | --------------- | ------ | ------------- | --- | -------- | ------ |
2https://platform.openai.com/docs/models/
| gpt-5.2 | | | | | | | framework | (Sheng | et al., | 2024). | Qwen2.5-3B-Instruct, | |
| ------- | --- | --- | --- | --- | --- | --- | --------- | ------ | ------- | ------ | -------------------- | --- |
3https://serper.dev/ Qwen2.5-7B-Instruct,andQwen2.5-14B-Instructserveas
| | | | | | | | the backbone | models, | trained | with | a learning | rate of 1e- |
| --- | --- | --- | --- | --- | --- | --- | ------------ | ------- | ------- | ---- | ---------- | ----------- |
5
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
Table4.Results:ExactMatch(EM)Accuracy(%)onvariousQAdatasets.O2-Searcherappliescold-startSFTbeforeRL,whileour
method(ICRL)appliesRLwithoutSFT.
| | Model | | Method | | SFT | | DifficultQuestionAnswering | | | | Average | |
| --- | ---------- | --- | ----------- | --- | --- | -------- | -------------------------- | ----- | ------- | --------- | ---------- | --- |
| | | | | | | TriviaQA | HotpotQA | 2Wiki | Musique | Bamboogle | | |
| | | | O2-Searcher | | ✓ | | | | | | | |
| | | | | | | 59.7 | 38.8 | 37.4 | 16.0 | | 34.4 37.26 | |
| | Qwen2.5-3B | | | | ✗ | | | | | | | |
| | | | ICRL | | | 72.6 | 35.4 | 39.2 | 20.0 | | 33.6 40.16 | |
Table5.Exampleofmulti-turnsearch-augmentedquestionanswering.ThemodelcorrectlyidentifiesGeorgeWashingtonasthepresident
whosetthetwo-termprecedentandretrieveshisinaugurationdatethroughtwosearchqueries.
Question Whendidthepresidentwhosettheprecedentofatwotermlimitenteroffice?
| | GoldenAnswer | | | April30,1789 | | | | | | | | |
| --- | ------------ | --- | --- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- |
ModelResponse <think>Ineedtoknowwhichpresidentsetthisprecedent. Ialsoneedtoknowwhenhe
enteredoffice.Letmestartbyfindingoutwhichpresidentitis.</think>
<search>presidentwhosettwotermlimitprecedent</search>
<information>Doc1(Title:“Twenty-secondAmendmenttotheUnitedStatesConstitution”)
Untiltheamendment’sratification,thepresidenthadnotbeensubjecttotermlimits,butboth
GeorgeWashingtonandThomasJefferson(thefirstandthird...Doc2(Title:“Amendment22
–TermLimitsforthePresidency”)ThedecisionforGeorgeWashington–theFirstPresident
of the United States – to voluntarily step down from office after two terms, established an
unofficial...Doc3(Title:“ThefirstU.S.presidentsethisowntermlimit”)Washingtonwrote...
</information>
<think>ItseemsGeorgeWashingtonsettheprecedent. NowIneedtofindoutwhenhe
enteredoffice.</think>
<search>whendidGeorgeWashingtonenterofficeaspresident</search>
<information>Doc1(Title: “PresidencyofGeorgeWashington”)Washingtonwasinau-
guratedasthefirstPresidentoftheUnitedStatesonApril30,1789... Doc2(Title: “George
Washington”)Washingtonwasunanimouslyelected... Doc3(Title:“PresidentWashington’s
FirstTerm(1789-1792)”)GeorgeWashingtonwasinaugurated...</information>
<think> Based on the information, George Washington entered office on April 30, 1789.
</think>
<answer>April30,1789</answer>
6. For each query, we sample 8 rollout trajectories with (EM)scoreof40.16,surpassingthebestcompetingmethod,
atemperatureof1.0tocomputethegroup-relativeadvan- Search-R1 (31.10), by +8.94. The improvements are es-
tage. Themaximumpromptlengthissetto5000tokensto peciallypronouncedonmulti-hopdatasetssuchas2Wiki
accommodatefew-shotdemonstrations,andthemaximum (+7.3),Musique(+9.7),andBamboogle(+7.2),demonstrat-
response length is capped at 2048 tokens, allowing up to ing ICRL’s strength in handling complex reasoning and
| 6searchturnsperquery. | | | Toregularizethepolicy, | | | | weap- tool-usescenarios. | | | | | |
| --------------------- | ------- | ---- | ---------------------- | --- | --------- | -------- | ------------------------ | --- | --- | --- | --- | --- |
| ply a KL | penalty | with | a coefficient | | of 0.001. | Training | is | | | | | |
Furthermore,onQwen2.5-7B,ICRLachievesanaverage
conductedon4NVIDIAA100GPUs(80GBeach),using
| | | | | | | | EMscoreof49.12, | | outperformingthestrongestbaseline, | | | |
| --------------- | -------- | ------------------------------- | -------- | ------------- | --- | ----------- | --------------- | --- | ---------------------------------- | --------- | ----------- | ------------ |
| abatchsizeof64. | | WeadoptFullyShardedDataParallel | | | | | | | | | | |
| | | | | | | | ParallelSearch | | (41.78), | by +7.34. | It achieves | the best re- |
| (FSDP) | training | with | gradient | checkpointing | | to optimize | | | | | | |
sultsonfouroutoffivedatasets,includingTriviaQA(75.4),
| memoryusage. | | Forretrieval,weuseaBM25retrieverthat | | | | | | | | | | |
| ------------ | --- | ------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
2Wiki(53.6),Musique(26.0),andBamboogle(48.0).These
returnsthetop-3documentsforeachsearchquery.
resultsshowthatICRLscaleseffectivelywithmodelsize
| | | | | | | | and | generalizes | well | across both | in-domain | and out-of- |
| --- | --- | --- | --- | --- | --- | --- | --- | ----------- | ---- | ----------- | --------- | ----------- |
3.2.MainResults
| | | | | | | | domainQAtasks. | | Theconsistentgainsoverbaselinesthat | | | |
| --- | --- | --- | --- | --- | --- | --- | -------------- | --- | ----------------------------------- | --- | --- | --- |
relyonsupervisedfine-tuningorrewardmodeling—such
Table3presentsthemainresultscomparingICRLtoother
baselines across five popular QA benchmarks. From the asZeroSearch,Search-R1,andRejectSampling—highlight
results,wecanobservethat: theeffectivenessofourin-contextreinforcementlearning
frameworkinlearningtool-usebehaviorswithoutexplicit
accuracy-basedrewardsorsupervision.
| ICRL | achieves | state-of-the-art | | | performance | across | | | | | | |
| -------------- | -------- | ---------------- | -------- | --- | ----------- | ---------------- | --- | --- | --- | --- | --- | --- |
| QA benchmarks. | | | As shown | in | Table | 3, ICRL signifi- | | | | | | |
ICRL
cantlyoutperformsallbaselinesonbothQwen2.5-3Band achieves better performance without SFT or la-
Qwen2.5-7BmodelsacrossfivechallengingQAdatasets. beleddata. Table4highlightsakeyadvantageofICRL:
On Qwen2.5-3B, ICRL achieves an average exact match it achieves superior performance without requiring any
6
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
(a)EMAccuracy
(b)FinishPercent
100
| | | | | | | 3~2~0 | | 100 | | | | | | |
| --- | --- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
3~2~1~0
| 80 | 75.4 | | | | | | | | | | | | | |
| ------------- | ---- | --- | --- | --- | --- | --- | --- | ------------------- | --- | --- | --- | --- | --- | --- |
| )%(ycaruccAME | | | | | | | | )%(hsiniFevitalumuC | | | | | | |
80
60
53.6
| | | | | | | 48 | | 60 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
42.6
40
3~2~0
| | | | | 26.8 | 26 | | | 40 | | | | | | |
| --- | ---- | --- | ---- | ---- | --- | ---- | --- | --- | --- | --- | --- | --- | ------- | --- |
| | 20.8 | | | | | | | | | | | | 3~2~1~0 | |
| 20 | | | 17.8 | | | 14.4 | | | | | | | | |
20
9
0
| | A | A | Wiki | | Que mboogle | | | | 0 | | | | | |
| ------ | -------- | --- | ---- | ---- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Trivia | Q Hotpot | Q | | MuSi | | | | | | | | | | |
| | | | 2 | | | | | | 1 | 2 | 3 | 4 | 5 6 | 7 |
Ba
NumberofTurns
Figure2.ComparisonofQwen-7Btrainedforthreestages(3~2~0)vsfourstages(3~2~1~0).(a)EMAccuracyacrossfiveQAdatasets.
(b)Cumulativefinishpercentvsnumberofsearchturns,aggregatedacrossalldatasets.
Table6.Results:Qwen2.5-14BmodelsExactMatch(EM)Accuracy(%)onvariousQAdatasets.Thebestperformanceissetbold.
DifficultQuestionAnswering
| | | Model | | Method | | | | | | | | Average | | |
| --- | ----------- | ----- | --- | ------ | -------- | ---- | -------- | ----- | ------- | --------- | ---- | ------- | --- | --- |
| | | | | | TriviaQA | | HotpotQA | 2Wiki | Musique | Bamboogle | | | | |
| | | | | Direct | | 52.0 | 22.6 | 28.2 | 6.0 | | 15.2 | 24.80 | | |
| | Qwen2.5-14B | | | CoT | | 56.4 | 24.6 | 25.8 | 9.0 | | 40.0 | 31.16 | | |
| | | | | ICRL | | 75.0 | 43.2 | 61.8 | 25.6 | | 53.6 | 51.84 | | |
SFT,incontrasttoO2-Searcher,whichappliesacold-start throughin-contextreinforcementlearning.
| SFT phase | before | reinforcement | | learning. | | Despite | using | | | | | | | |
| ---------- | ------ | ------------- | ---------------- | --------- | ------------ | ------- | ----- | --- | --- | --- | --- | --- | --- | --- |
| no labeled | tool | traces | or task-specific | | supervision, | | ICRL | | | | | | | |
4.FurtherAnalysis
achievesahigheraverageEMscoreof40.16comparedto
37.26 from O2-Searcher. It outperforms O2-Searcher on 4.1.Ablationanalysis
fouroutoffivedatasets,includingsubstantialgainsonTrivi-
| | | | | | | | | Ablation | on curriculum | | design | for | rollout reduction. | |
| --- | --- | --- | --- | --- | --- | --- | --- | -------- | ------------- | --- | ------ | --- | ------------------ | --- |
aQA(+12.9)andMusique(+4.0).Theseresultsdemonstrate
Weconductanablationstudycomparingtwocurriculafor
thatICRLcanlearneffectivetool-usestrategiespurelyfrom
reducingthenumberofexamplesusedintherolloutprocess:
in-contextexamplesandreinforcementsignals,offeringa
athree-stageschedule(3~2~0)andafour-stageschedule
scalableanddata-efficientalternativetomethodsthatrely
| | | | | | | | | (3~2~1~0). | AsshowninFigure2(a),thethree-stagevariant | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | ---------- | ----------------------------------------- | --- | --- | --- | --- | --- |
oncostlyannotationandpretraining.
achievessubstantiallyhigherEMaccuracyacrossallfive
| | | | | | | | | QA datasets. | For | instance, | on | TriviaQA | and 2Wiki, | the |
| --- | --- | --- | --- | --- | --- | --- | --- | ------------ | --- | --------- | --- | -------- | ---------- | --- |
3.3.ConcreteExamples
three-stagemodelreaches75.4and53.6,comparedto20.8
Table5presentsacompletereasoningexamplefromICRL- and26.8withthefour-stageversion.
| Qwen2.5-7B | on | a question | | from the | Bamboogle | dataset. | | | | | | | | |
| ---------- | --- | ---------- | --- | -------- | --------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
Figure2(b)showsthatthefour-stagecurriculumleadsto
Themodelistaskedwithansweringacompositionalquery:
fasterdecisions,withover80%ofqueriesfinishingwithin
| identifying | the | president | who | established | | the two-term | | | | | | | | |
| ----------- | --- | --------- | --- | ----------- | --- | ------------ | --- | --------------- | ---------------------------------- | --- | --- | --- | --- | --- |
| | | | | | | | | twosearchturns. | However,thiscomesatthecostofanswer | | | | | |
precedentanddeterminingwhenheenteredoffice. Itfirst quality. These results suggest that aggressively reducing
issuesasearchquerytoidentifytherelevantfigure,correctly
| | | | | | | | | rollout length | too | early | (via the | intermediate | stage | with |
| ---------- | ---- | ------ | ---------- | --- | ------- | ---------- | --- | -------------- | --- | ----- | -------- | ------------ | ----- | ---- |
| concluding | that | George | Washington | | set the | precedent. | It | | | | | | | |
oneexample)encouragesprematurestoppingandweakens
thenformulatesafollow-upquerytoretrievehisinaugura-
| | | | | | | | | multi-turnreasoning. | | Incontrast,thesimpler3~2~0curricu- | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | -------------------- | --- | ---------------------------------- | --- | --- | --- | --- |
tiondateandsuccessfullyextractsthecorrectanswer,April
lummaintainsstrongerperformancebyallowingthemodel
30,1789. ThiscaseillustratesICRL’sabilitytodecompose
toexplorelongerreasoningpathsduringtraining.
complexquestions,retrieverelevantinformationacrossmul-
tipleturns,andmaintaincoherentreasoningwithoutexplicit
intermediatesupervision. Itdemonstratestheeffectiveness ModelScalingand14BPerformance. Toevaluatethe
ofourframeworkinlearningstructuredtool-usebehaviors scalabilityofICRLacrosslargermodels, weappliedour
7
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
| | (a)ResponseLength | | | (b)Reward | | (c)NumberofValidSearch | | | |
| --- | ----------------- | --- | --- | --------- | --- | ---------------------- | --- | --- | --- |
Figure3.Trainingdynamicscomparisonacrossdifferentfew-shotsettings(3-shot,2-shot,0-shot)forQwen-7Bmodel.
Table7.Results:Accuracy(%)onMathQAdatasets.
methodtoQwen2.5-14B-InstructandreportresultsinTa-
ble6.ICRLsignificantlyoutperformsbothdirectprompting
MathQA
andCoTmethods acrossallfiveQAdatasets. Inparticu- Model Method SFT
| | | | | | | | AIME2024 | AIME2025 | |
| --- | --- | --- | --- | --- | --- | --- | -------- | -------- | --- |
lar, itachieves75.0EMonTriviaQAand61.8on2Wiki,
| | | | | | ReTool | ✓ | 67.0 | | 49.3 |
| ----------------------------------------------- | ------------------- | --------- | --------------- | -------- | ------ | --- | ---- | --- | ---- |
| yieldingastrongaverageEMscoreof51.84—surpassing | | | | Qwen3-8B | | | | | |
| | | | | | ICRL | ✗ | 64.1 | | 51.7 |
| CoT | by +20.7 and direct | prompting | by +27.0. These | re- | | | | | |
sultsdemonstratethatICRLcontinuestoscaleeffectively
tolargermodelsizesandbenefitsfromincreasedcapacity,
compareourresultswithReTool,whichisaSFT-RLtrain-
withoutrequiringadditionalsupervisionorannotation.
ingframeworkthattrainingmodelstolearncode-writing
| | | | | and tool-calling | on running | code. | ReTool | (Feng | et al., |
| --- | --- | --- | --- | ---------------- | ---------- | ----- | ------ | ----- | ------- |
4.2.Trainingprocess
| | | | | 2025) achieves | the state-of-the-art | | performance | on | code- |
| --- | --- | --- | --- | -------------- | -------------------- | --- | ----------- | --- | ----- |
TounderstandhowICRLevolvesduringtraining,weana-
augmentedlong-formreasoningforsolvingmathproblems,
lyzethelearningcurvesacrossthe3-shot,2-shot,and0-shot butitalsoneedsalotofannotateddatatoapplycold-start
curriculumstagesusingtheQwen2.5-7Bmodel.Intheearly
SFTformodelstolearnthetool-callingformatandunder-
stageswithdemonstrations(3-shotand2-shot),themodel standthewholereasoningprocess. However,ourmethod
producesrelativelystableandwell-structuredresponses,as ICRLdoesn’tneedtoSFTthemodelfirst,andmodelscan
| reflected | in the consistent | response | lengths. As training | | | | | | |
| --------- | ----------------- | -------- | -------------------- | --- | --- | --- | --- | --- | --- |
stillreasoningproperlyfromtheIn-ContextLearningwith
progressesintothe0-shotstage,theresponselengthinitially the examples we provides in prompt. From Table 7, al-
dropsduetotheremovalofin-contextexamplesbutgradu-
throughoutmethodunderperformsReToolonAIME2024
allyincreasesagain,indicatingthatthemodelislearningto by 2.9%, it can achieve better result on AIME2025 with
independentlycomposelongerandmorestructuredoutputs.
| | | | | +2.4%accuracy. | Whichmeansourmethodstillworksfor | | | | |
| --- | --- | --- | --- | -------------- | -------------------------------- | ------------------ | ---------- | --- | ------ |
| | | | | help models | to learn | other tool-calling | operations | | and is |
Althoughtherewardremainsrelativelysteadythroughout
moredata-efficientthanothermethodsthatneedcold-start
trainingandisbasedonlyonsparsesignals—outputformat
validityandfinalansweraccuracy—themodelstilllearns SFTwiththousandsofannotateddata.
| tousetoolsmoreeffectivelyovertime. | | | Thisismostclearly | | | | | | |
| ---------------------------------------------------- | ------------------------------------ | --- | ----------------- | ------------ | --- | --- | --- | --- | --- |
| reflectedintheincreasingnumberofvalidtoolcallsduring | | | | 5.Conclusion | | | | | |
| the0-shotphase. | Theriseinvalidtoolusageindicatesthat | | | | | | | | |
WeintroduceICRL,asimpleyetpowerfulframeworkfor
ICRLsuccessfullyencouragesthemodeltointernalizetool-
usebehavior,evenwithoutdenseorstep-levelsupervision. training LLMs to use tools via in-context reinforcement
| | | | | learning,withoutrequiringSFTorlabeledtooltraces. | | | | | By |
| --- | --- | --- | --- | ------------------------------------------------ | --- | --- | --- | --- | --- |
incorporatingfew-shotdemonstrationsdirectlyintotheRL
4.3.Generalize
rolloutpromptsandgraduallyphasingthemout,ICRLen-
Apart from conducting our method in web search tool- ablesmodelstotransitionfromimitationtoautonomoustool
callingdomain,wealsoevaluateourmethodbytrainingthe
| | | | | usethroughreward-drivenlearning. | | | Ourmethodachieves | | |
| --- | --- | --- | --- | -------------------------------- | --- | --- | ----------------- | --- | --- |
models’abilitiesoncode-writingandcallingtoolstorunthe strong performance across a range of QA and reasoning
pythoncodetohelpthemsolvecomplexmathproblems.We benchmarks, outperforming existing approaches that rely
8
In-ContextReinforcementLearningforToolUseinLargeLanguageModels
onsuperviseddataorfrozentool-usepolicies. ICRLalso Hsiao, V., Fine-Morris, M., Roberts, M., Smith, L. N.,
generalizesacrossdomains,includingwebsearchandcode and Hiatt, L. M. A critical assessment of LLMs for
execution, demonstrating its flexibility and effectiveness. solving multi-step problems: Preliminary results. In
TheseresultshighlightICRLasascalableanddata-efficient AAAI 2025 Workshop LM4Plan, 2025. URL https:
alternative to traditional SFT+RL pipelines for enabling //openreview.net/forum?id=kFrqoVtMIy.
tool-augmentedlanguagemodels.
| | | | | Jin, B., Zeng, | H., | Yue, Z., | Yoon, | J., | Arik, S., | Wang, D., |
| --- | --- | --- | --- | -------------- | ------- | -------- | ------------- | --- | --------- | --------- |
| | | | | Zamani, | H., and | Han, | J. Search-r1: | | Training | llms to |
ImpactStatements
reasonandleveragesearchengineswithreinforcement
Thispaperpresentsworkwhosegoalistoadvancethefield learning. arXivpreprintarXiv:2503.09516,2025a.
| of machine | learning. There | are many potential | societal | | | | | | | |
| ---------- | --------------- | ------------------ | -------- | --- | --- | --- | --- | --- | --- | --- |
consequencesofourwork,noneofwhichwefeelmustbe Jin, J., Zhu, Y., Dou, Z., Dong, G., Yang, X., Zhang, C.,
| | | | | Zhao, T., | Yang, | Z., and | Wen, | J.-R. | Flashrag: | A mod- |
| --- | --- | --- | --- | --------- | ----- | ------- | ---- | ----- | --------- | ------ |
specificallyhighlightedhere.
ulartoolkitforefficientretrieval-augmentedgeneration
| | | | | research. | InCompanionProceedingsoftheACMonWeb | | | | | |
| --- | --- | --- | --- | --------- | ----------------------------------- | --- | --- | --- | --- | --- |
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