*KKV9KKQ ݰࠆֹלܹऱͧНѽ؈ৗ୼ԇҵࣁ ‘/ ֤

Ծٶ  ߃ͫ*KKV9KKQ ݐӟङ *KKV9KKQ< ֨Ҷࣱ ‘/ ௅ֿ㟬૜д ٚםङࡦ䊦ͫ؉љߢѺङઐীۨߎؘͫ࣫дЊ -6:U չ )RG[JK 9UTTKZ  ঈ஽طֺࠥबど২ङۅਈͫ஭ۖдЏउ澞

偽媮䣐ㅿ㗽⫸㩣㤠⚊廚坋㳰ⶥ壢嫺漓䐧㗿䩿≔䗳䕼䖃嫬壿攏惉⁵⪝䊯ṍㄏ 㘫⎋槗㑇俼䖃ㅿ㘮俋㕮漏㾸⃺⋮㛤䘊漐

չЇࠩЉգङީͫ଑ࠩݐӟङޏֺࠥ *KKV9KKQ8 ЉюۨߎѺͫ޾ީ ֨܉ߐЇ߄дםक़ݕԟ

৲Ќͫ؉ଐީ▲Зڐֺ࢛ࠥ

଑࠮ޏֺࠥڍ৒дҿ௤ۅџࡁङѩԎͫюऀԝӣФ▲ङۨߎؼଇӱд -6:U ঴Ӱङ੮࣫

۱љͫڮךЏӄыבࣾਙ֓ӟдȔ*KKV9KKQ ݎࣰ 5VKT’/ȕङՍ՚ ࡁײͫӹ 3KZG ‘/ ٗҁыմ澝ऽդ ‘/ ખކݐࣔҁৱ +R\OY ؼڠલ *KKV9KKQ8 ࣩ੥ݧׅवࣇ֛ؖͧН؈ܮॺгܶԞל੶੗ࠆֹܱࣂৗ ԂࣩיमݱࡄͧٶՆࢻгҾЗޟރगࣩࡦࢻࢤ㓬

TheDeepSeek-R1paperisagem! DeepSeek-R1论文堪称瑰宝！

Highly encourage everyone to read it. 强烈建议大家阅读

It’s clearthat LLMreasoningcapabilities can be learned in different ways.

显然， LLM的推理能力可以通过不同的方式学习

RL,if applied correctlyand atscale,canlead tosomereallypowerful and interestingscalingandemergentproperties.

如果正确且大规模地应用强化学习（RL），可以带来一些非常强大且有趣的扩展和涌现特性。

Ր▲ѹ ‘/ ֥ם < ?[INKT 0OT өઍОͫ*KKV9KKQ8 ખކИݕӟङͫࠥ ֺӯऀষ 82 ސࡣږحҿਘП؆ЮչՆۃݐࣲ଑▲Շ࣫ͫ۞Уளٯ୍ם

This"Aha moment"in the DeepSeek-R1paperis huge:

Purereinforcementlearning(RL)enables anLLM to automaticallylearn to think and reflect.

This challenges the priorbelief that replicating OpenAl’s o1 reasoning modelsrequiresextensive CoTdata.It turnsoutyoujustneed togive it the right incentives.

We are so back in the AlphaGo excitement era:by playing countless Go games and maximizing the reward function (winning the game) using pure RL,AlphaGobeat the best human players.

Now we areentering theLLM RLera.
2025couldbetheyearof RL.

DeepSeek-R1论文中的这一“顿悟时刻”意义重大：

纯强化学习 (RL) 使LLM能够自动学会思考和反思这挑战了先前认为复制OpenAI的o1推理模型需要大量CoT数据的观点。事实证明，只需给予正确的激励即可，

ਸ਼ѭଇ -+‘8 2GH ாऩિુы 0OS ,GT ֨ݐࣔИЭݕӱдͫ*KKV9KKQ8 ऀପଋॆ৚ुઁөઋ঑ӟङऱؘ׫ԋͫ৲ହҲ҅ऀѠѾ 82 ؠޣॄઆङ ؆Ю׫ԋֺࠥ澞଑҅ڱֺࠥфࣿдਘ۩ՆۃЊݍফ੧Оङࢅ࣫澞0OS ,GT ࣾਙઍОͫ؉ћҟд 5VKT’/ ߎߛځથҟङзͫڐ࢛

JimFan

@DrJimFan

We are living in a timeline where a non-US company is keeping the original missionofOpenAlalive-trulyopen,frontierresearchthat empowers all.Itmakes nosense.Themostentertainingoutcomeis the most likely.

我们正处在一个时间线上，一家非美国公司正在延续OpenAl的原始使命真正开放、前沿的研究，赋能所有人。这毫无道理。最有趣的结果也是最有可能的。

ଊСୋங޼гͧєњےܶӰࣩআ 82 ݱࡄ੟এࠆֹފܗьСͺࠆֹӞࢻࣩ Ȑ’NG 3USKTZȑͧՀӚьСৗ੩ރ ‘/ ҿޥгࡦࢻৗԂͺۊњޟ㖸ऍଂ ࣩފͧ*KKV9KKQ8 ࣩૠ▁ଜੌӪݰتи ‘/ ஔ־ޭ޼ࣩՆىͧाौڿ յअьСͺ

⭘ᴰㆰঅⲴ䝽ᯩ എᖂᴰ㓟㋩ⲴᕪॆᆖҐ

֨ U ݐӟФեͫݐࣲڠԗۨдЏउ߂Ҽࡨङސࡣ▁ৱ޼੹ͧ▁Жࠆֹ֧੟এ૚सЗՐѪعੰ▁म֠ؓ੟এݱࡄ޼ܶԞܱࣂৗԂ

৲ *KKV9KKQ ֝஄֨ 8 ङઐীଋ३Иͫफݎ▲ࠩۅؘ௠дІय़۬ࣀЉգङ܉ߐ૨ڬ͹फݎڠԗ؆Юઐীͧ8@KXUͨ澝ךஉ࠼࢏଒ઐীͧ8չֺࠥ嘜楫ͫଐୃۨԅд澞ךஉ࠼࢏଒ઐীސࡣչֺࠥ嘜楫ୃԕխवڮךӫޏ۞Уҫপͫث੧Џ߄व୍੽ڧր

ҾЗޣਫ਼ъࢍԇࣩͧ૟ފࣻܯڟԖ؅ЭૠЖષګ澞֛Н *KKV9KKQ8 ފநЖ੩ރૠ▁ݱࡄޥݝࣩࠆֹ

۩ћүߛдઆ▲Јͫઐী ‘/ ङݐࣲਈԃѮ৏ङސࡣପٯީэТ͹▲ਢ ީପଋ֨ 9,:ͧडषڳલͨԆҵם୏ङۃ৓୥ͧ)5:ͨਸ҆ͫऀ҆ચչ זߒङײଋ३׫ԋֺࠥͧ683ͨФঝङזߒ॔৆ৠৌ׫ԋֺࠥͫߛએࠥ ֺ؆ѫऀۃ৓୥ۃৰ

ࣾਙѫԆҵ੏ࣔԪ࡯߹ݜফͧ3):9ͨͫએֺࠥ֨ךय़ՕਈИݜফ߂ױङ Օਈ澞

Ѯ৏ङֺࠥઐী૨ڬѸ *KKV9KKQ8@KXU ଣܫд▲ߚӹ۱ߌ߄ङ૨ڬȔষ

ڠԗ؆Ю૨ڬͫ؉؏Ҷܒڐд௄ગङۃ৓୥ࠥߡͧ)NGOT UL :NU[MNZ չडषڔڳલͧ9,:ͨͫю҉ழএԥङ׫ۚҒ՚ߛѩԗֺࠥ੧О ଑ؼҦએ▲Зמ۵ҩॿ࡚֨߄ѠѾਸ҆չܶحङەӑЈͫষণପଋЉލ غડչੂڱՆ௘ߛ؆Юઆொ
*KKV9KKQ8@KXU ߄ङՑީ▲׬߂এԥङ׫ԋ঩৏ͫߛࢬՇ ‘/ ङݐ ࣲਈԃ

ૠЖ੐ӨػГ޻

 ӕेۅ׫ԋ͹ӕेۅ׫ԋֺࠥછѳրځީի࠳े澞ثдؼԆӣͫ୪ дۻӣ澞છџސࡣЭڮএԥ͹҆ײͫ֨Ӏ߄ेؔۅৈߧङރ؆୼ொИ ֺࠥ஬੽љܶؔࠀڔͧײ"GTY]KX$չ"GTY]KX$୿ͨݕ҈߂ৄঋࠄ ثй৚३୼ொͫՕљ҅ऀ৚ઠ֘߿݇௄ؔУङࡹડऀ҆ࣿۨՆ௘

 ࠀڔ׫ԋ͹ࠀڔ׫ԋֺࠥڠӲ੽ࡌֺࠥرҿۃৰଋ३৥й"ZNOTQ$ չ"ZNOTQ$߶঎Ф୿澞࡚଑ТҟؼۻӣͫҟдؼԆӣ

Одӕे઀بֺࠥ֨ڠԗ؆Юͧ82ͨଋ३Иङਘࣀ଒يͫ*KKV9KKQ ࣾ ਙ߄۞ر঩৏ݕॐટю঳ߘஒӲ֨଑य़ৈߣࠀڔЇͫߛହҲѠѾӄؠࣔ ؔङҞ੿ȍȍ҆ײڠӲએֺࠥ଒੧Նۃۅݐࣲ۪ݐٺࣔؔङ୼ொઆӐ ঌऋ

ழव଑Т▲Зএԥङઁөͫએ ‘/ ֨ -865ͧ-XU[V 8KRGZO\K 6UROI_ 5VZOSO`GZOUTͨङઁөЈਘ۩୊߽ $^+$ ࡁૻͫਘ۩ݕԟ

-865 ङࠥڔҿؘࡁૻএԥͫପଋুӄ߽ߎङबثࡁૻߛઋ঑ঌऋࠐچ ߄ݼ஑ѺдઐীङЉ६ؔۅͫգޞݕ௤д؆Юݼࣤ

এԥߛપͫ҂Օљ܋؉۝઻ۨ৯٤ӟொͫ࠿ଳொએֺࠥգޞ֛ঋךࠩ ࣀեऀЇவङ׫ۚઁөো࠿Зঋࠄ۸ӣͫ߿݇ଝࡌ௤ӣ澝ହҲѺӣङମ ૾޾ޏֺࠥ澞ૠЖࡗसלࠃފૠߞࣩ͵૎Ҵୋங $\rightarrow$ ࠆֹ࣏ۉיЖग़ߥ $\rightarrow$ ੐Өॸঞ੪Ӣ $\rightarrow$ -865 ਗ਼ॠࣼتѨԍ $\rightarrow$ ޟݰࠆֹ଑य़फݎઐী ސࡣ٫ߛдәЗުੋङѩԎ澞௙үީઐীݼࣤङݕԟͫޅЗଋ३Օљ֨ ޾िङޞ୿ӄ؏ۨ澞ҿࠩީ૏࢛ࢃ৴ङ஑ѺͫंйमԾд 9,: չזߒङ ׫ֺۚࠥͫઋ঑૏࢛ङ஬ࡌםٱӗص

޾୍੽ङީͫૠमݱࡄँࣩਫ਼ࠆֹ؅Ѫг㓢িͧুЋފјȐ஑㕤ȑࣩݱ ړ؅Ѫࣩ澞

2

⭘㠚ᐡⲴ䈝䀰ˈ൘Ā亯ᛏāѝᆖҐ

۩ћީۂТरӟֺࠥ֨଑य़ளٯȔԽ׷ȕङސࡣЈͫީऱङ؆ѫдȔۃ ৰȕङ⪢;

ખކઓڣд▲Зږыࡨऩङࠄ҆͹֨הࣲ▲ЗࢄՃזߒރ؆੮ଇڔ ɰG  ${\surd}({\tt a}+{\tt x})={\tt x}$ ङ୼ொޞֺͫࠥॳࣀҠЈߛપ=GOZ ]GOZ =GOZ :NGZ Y GT GNG SUSKTZ / IGT LRGM NKXKͧঈঈ澝ঈঈ澝଑ީЗқڱ߶ઓङ֍ տޞӶͨͫசե୍ޏؙંдޅЗઆொଋ३澞଑य़ঝѷыঝூ㖂ङ੧О؏ ҶީਘՇфࣿङͫ৲Љީ௄үગؔङ

Question:If a>1,then the sum of therealsolutions of√a-√a+x=x is equal to

Response:

Tosolvetheequation√a-√a+x=x,let'sstartbysquaringboth.

a-√a+x =x²=a-√a+x=x².

Rearrangetoisolatetheinnersquarerootterm: （a-x²）²=a+x=→a²-2ax²+（x²）²=a+x=→x4-2ax²-x+（a²-a）=0

Wait,wait.Wait.That'sanahamomentIcanflaghere. Let'sreevaluate thisstep-by-step toidentify if thecorrect sum canbe.

Westartedwiththeequation:

√a-√a+x=x

First,let'ssquarebothsides:

a-√a+x=x²=√a+x=a-x²

Next,Icouldsquarebothsidesagain,treatingtheequation:

֜О߿݇ *KKV9KKQ ङू९ֺͫࠥङ଒࠵ٷள֮Ԕ࢏଒ङ澞֨ڠԗ؆Ю ଋ३Иͫրځ୸چѫӟ࣫ॳࣀङުੋ׍୸ͫ଑п૩ૣࢵڪڪѴசवઆ ொঌऋङૅՊ澞଑य़ࠥڔ懣ѷыঝ֨୸ߊۃৰեङॳࣀூ㖂ͫ޹ॐव߮ य़ࢋكङઍऽॳॄ

Figure 3|The averageresponse length ofDeepSeek-R1-Zeroon thetraining set during theRL process.DeepSeek-Ri-Zero naturally learns to solve reasoning tasks withmore thinking time.

֨଑य़Ѵசवூ㖂ङਈԃݕԟЈͫ8@KXU ֨ރ؆उц߄धઉङ ‘/3+ ॼ૔Иђ߂Ӭङ $15.6%$ ࠳ेࣤ▲૨㥁ԟਙ $71.0%$ ङӕेࣤ澞৲એֺࠥث գ▲୼ொ଒੧ךࠩغડޞͫӕेࣤࣾਙଇӱд $86.7%$ 澞଑Љީএԥङर ଋдؼѫҟдȍȍ֜О ‘/3+ ङொऩ஬੽ࢋچङރ؆फ઄չӫଭۅۃ ৓ͫ৲ЉީߑࠑۅङҸڔځऀ澞ֺׂࠥߎڷீਈݐࣲͫ۵Օਈ߄଑߽ङ ݕԟ澞

Figure 2|AIME accuracy of DeepSeek-R1-Zero during training.For each question,we sample 16 responses and calculate the overall average accuracy to ensure a stable evaluation.

Ր▲Зֺࠥेؘପଋ଑य़ސࡣ؆ѫдݐࣲङՐ▲З߾ڶચ݇ͫީֺࠥր ځ୸چѫ߿݇୼ொङזߒچਘࣀલਭ澞଑य़ਘଠځ੧О੮ޢͫ؉Љީ֨ এԥ֪׬ऀࠥߡͫ৲ީऱ࠳ࣲઆд୼ொङ஡چͫٷबځ֪܎ҵ޾ךङ ۃৰޞ୿澞ؼҦыঝவثএԥङԆࡣչזߒङ॥ӣѫਘࣀલޅۃৰޞ ୿▲߽ͫ8@KXU ي࣫ӟдঝѷङ޴㘸

߂߄પ߆ԃङ۪કީֺࠥي࣫ӟङଈ१؆Юਈԃ澞֨؏ҶЉգङ৚३ॼ૔ٵՖ )UJKLUXIKY Їͫ8@KXU ଇӱд૝ଋ $96.3%$ ыঝଣ۴ङࡊٵ଑य़૧ֿ੮࣫੮ޢֺͫࠥЉީ֨࠸ઓॆਅࣔؔ௅ֿङઆொ܉ٙͫ৲ީ݋ݗд߮य़ޯଠङݐࣲਈԃ

ањ㚚᰾ˈնਓ喯н␵Ⲵཙ᡽

ـ঒ 8@KXU ي࣫ӟдۖыङݐࣲਈԃͫѸू९ৱћڮڽՇ࣫д▲З Е୍ङ୼ொ͹؉ङۃ৓ଋ३ڪڪ஡љੴыঝࣲઆ

ખކַ峭֪ܶӟͫ଑Зষڠԗ؆Юઐীӟߛङֺࠥ؂֨VUUX XKGJGHOROZ_ͧՕયۅٛͨչRGTM[GMK SO^OTMͧધઈࢌߒͨङ୼ொ ଑З࣫઻ҿؘڮױࣲઆ͹8@KXU ؏Ҷପଋ׫ۚҒ՚ߛѩԗҿ੧О࡚ͫ ߄ѠѾыঝॐਸङ߶ӕঋࠄҁОՀৰ澞ؼҦ▲Зמ۵ҩॿਘӫд▲׬ આொސࡣͫ੝ࣀ㇊ડЉ䔦ͫѸէӰыઆୋޞ԰ધޗѰࠩ澞؉֨આொଋ३ ИՕਈգޞ҅ऀךय़ધઈ۪ͫৱՇيӟд߮य़ࣔ࠺ङ੮ଇސڔͫ଑пୃ એҿݐࣲଋ३஡љੴଝ૭չࣲઆ

࠳ީОдઆӐ଑З୼ொͫू९֝஄ڐՇдݷ଒࣍ߎ *KKV9KKQ8澞ପ ଋږҵ޾Ѯ৏ङIURJYZGXZ JGZGͧӒկԈރ݇ͨչךஉ࠼ઐীࡶ३ 8 Љюґܴдڠםङݐࣲਈԃͫଐ؆ѫдऀыঝޣۤङސڔ੮ଇۃ৓ ଋ३澞଑ؼҦো଻Зמ۵ҩॿ୆д▲З㿺ପݾীͫݾѫѕײѾࢎޱ֪੮ ଇਘٜङ۝ࡣ

֨଑▲લݾЈФեͫ*KKV9KKQ8 ي࣫ӟдЊ 5VKT’/ U बڢࣾਙ֨ ߮пސவ޾ѩङۅਈ澞֨ 3’:. ׂӕࡹડЇͫ8 ଇӱд $77.5%$ ङӕे ࣤͫЊ U ङ $77.3%$ ब଎ͺ֨޾Ӏܸ۫ۅङ ‘/3+  Їͫ8 ङӕे ࣤଇӱ $71.3%$ ͫ૝ଋд U ङ $71.0%$ 澞֨їु௅ֿͫ8 ֨ )UJKLUXIKY છࡹИଇӱд  ӣङࡊٵͫ௤й $96.3%$ ङыঝՀЊৱ

Table 4 | Comparison between DeepSeek-R1 and other representative models

	Benchmark (Metrio)	Claude-3.5- Sonnet-1022 0513	GPT-4o DeepSeek|OpenAI OpenAI|DeepSeek V3		ol-mini 01-1217		R1
	Architecture			MoE			MoE
	Activated Params			37B			37B
	#Total Params			671B			671B
	MMLU (Pae1)	88.3	87.2	88.5	85.2	91.8	90.8
English	MMLU-Redux (EM)	688	088	89.1	86.7		92.9
	MMLU-Pro (EM)	78.0	72.6	75.9	80.3		84.0
	DROP (3-shot F1)	88.3	83.7	91.6	83.9	90.2	92.2
	IF-Eval (Prompt Strict)	S'98	84.3	86.1	84.8		83.3
	GPQA Diamond (Pasa)	65.0	49.9	59.1	60.0	75.7	71.5
	SimpleQA (Corect)	28.4			7.0	47.0	30.1
	FRAMES (A)	72.5	80.5	73.3	76.9		82.5
	AlpacaEval2.0 (LC-wirak)	52.0	51.1	70.0	57.8
	ArenaHard (GPT-4-1106)	85.2	80.4	85.5	92.0		92.3
Code	LiveCodeBench (Pasl-con)	689	32.9	36.2	53.8	63.4	65.9
	Codeforces (Pereentsle)	20.3	23.6	58.7	93.4	96.6	96.3
	Codeforces (Rating)	717	759	1134	1820	2061	2029
	SWE Verified (Resalved)	50.8		42.0	41.6	48.9	49.2
	Aider-Polyglot (Ac.)	45.3	16.0	49.6	32.9	61.7
Math	AIME 2024 (ras)	16.0	9.3	39.2	63.6	79.2	79.8
	MATH-500 (Pas@1)	78.3	74.6	90.2	90.0	96.4
	CNMO 2024 (Pa1)	13.1	10.8	43.2	67.6
Chinese C-Eval (EM)	CLUEWSC (IM)	85.4	87.9	6'06	89.9		92.8
		76.7	76.0	86.5	68.9		91.8
	C-SimpleQA (Coect)	55.4	58.7	68.0	40.3		63.7

ࣀ৲ͫ*KKV9KKQ8 @KXU ङࢪԃѷЧ޾ם澞؉֨ ‘/3+  ࡹડИ ҅ऀךރ܎ॕߑӲޞଇӱङ ӕेࣤȍȍ଑Зۨ働ࣾਙ૝ଋд 5VKT’/ ङ U澞଑य़ךࠩغડѫՊڱ޾ӕेङࣔګͫ޹ॐ 8@KXU Օਈ݋ݗд߮य़ׂॅङݐࣲࠃ߫ͫ৲Љީএԥ֪ઓڸઆொࠥڔ ખކރ݇ުॐͫђ 3’:. ӱ ‘/3+ͫӇӱ -931ֺͫࠥ੮࣫ӟ६ ؔङ૧ֿۅਈͫࣔӰީ֨஬੽ӫଭۅۃ৓ङזߒ୼ொЇ澞଑य़ٺ崭ۅਈ ݕॐ 8@KXU Օਈेׁؘ⡭ӟд߮य़ׂॅङݐࣲਈԃͫ଑ЊѮ৏ङࣔ ؔѠԇѩԗֺࠥڥۨ௪ޢثࡁ

۱љͫ੝ࣀՍ௸ЉࢎͫѸЭક *KKV9KKQ8@KXU ۵ީऱ࠳ࣲઆдݐࣲङȔמ۵ȕ

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ҏ䇨᡽ᱟ䙊ੁ $*, Ⲵ᜿ཆᦧᖴ

Ф۱љ *KKV9KKQ8 ङՇ٢એ֥ӄыङࢾࢵୃ܎էдষڠԗ؆Юސࡣ ֜О؉؏ҶՕљપڱЇީ۸ڐд ‘/ ଒ԗङ▲ߚޏ૨ڬ

8@KXUȍȍ଑З؏Ҷପଋڠԗ؆Юઐীӟߛङ ‘/ ֺࠥͫي࣫ӟдј ыۖ峖ङପऀݐࣲਈԃ澞؉Љю֨ރ؆ॼ૔ИՈڱдۖыۨ働

޾୍੽ङީͫ8@KXU Љюީ֨ࠥѢۃৰͫ৲ީऱ࠳Շيӟд߮य़ڥڔ ङݐࣲਈԃ

֜О֨ଋڪङઐীސࡣИͫػҿ֨डषڳલИ҅ऀઐীױङ॔৆ৠৌߛ છѳૅ୏ङઢֺͫࠥՕਈ؆ѫઇՇ׫ԋֺࠥङࣔؔࠥڔͫࣿۨث׫ԋࠥ ֺՍնङӄؠͫ৲Љީऱ࠳ݕԟݐࣲਈԃ澞݅Տઢપͫ’/ ঩৏܇ӱд ੂڱ௤׫ԋѸؘஎЇଔਅઐীऩ߶ङ܎ߑՈٙސڔ澞଑ؼީ۩ћٯપङ ׫ԋ࠭௡ͧXK]GXJ NGIQOTMͨ澞ѷ 8@KXU ࣐߃फ़ࣩתԊ੐Өׁޯଈ ұгתԊࠎரࣩՔৗ㓬ȉȉ੐Өמफ़Ԥгͧ࠻ޥьСȐՌյȑՔјԽࠆ ѡ澞ࠆֹ֧ૠЖ㖊ӐЇՆىӞࣩܱࣂৗԂޟՔґͧЬޟ১࢐

଑ЗՇ࣫ՕਈѫݷՊ۩ћثߑ֘؆Юङઍજ͹Ѯ৏ङ ‘/ ઐীސࡣՕਈ ▲फ୍֨ז▲З߿ߎۅङ୪નͫ۩ћןЋࡨйએ ‘/ ࠥѢыঝङۃ৓ސ ڔдͫЏउ஬੽୍ޏۃৰडष؆Ю֨ ‘/ ՇيИङઅ਩澞ପଋষণङڠ ԗ؆Юͫ’/ ঩৏ѷЧਈלՇيӟ޾Խࣿङ୼ொઆӐਈԃͫ৲Љީੴஒ Ӳ֨௄ગङઆӐސࠄࠃ߫ӄ

੝ࣀ 8@KXU ֨૿ӟՕયۅЇ؂֨ޢު৞஘ͫѸ଑З৞஘ߎ૯Օਈۏ ۏԭચдҿۃ৓ސڔङࣞࣔۅ澞ؼҦ▲Зמ۵ҩॿՇޢдਘٜङઆொސ ࡣͫ԰஡љऀٯઁધઈઆୋ▲߽澞଑ݕॐ۩ћ͹ऱ࠳ङପऀыٗ޴ਈՕ ਈ஬੽؏ҶЉգйыঝङઍऽސڔ

ૠۖފँࠔࣩڟԖ؅Э澞ػҥਚգݟ৒؞࣬н߀ࣩࣂ੥͵ँࠔࣩࣂ੕޼ ১иОԇڎ߄ͧুЈފ੃ԇܯՈ