Poly-EPO: Training Exploratory Reasoning Models

Poly-EPO: Training Exploratory Reasoning Models | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-21
Score updated: 2026-04-21
Score fresh until: 2026-05-21
References: 0
Source count: 4
Coverage: 50%

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Poly-EPO: Training Exploratory Reasoning Models

PDF: https://arxiv.org/pdf/2604.17654v1

Repository: https://github.com/goodfeli/dlbook_notation

Source count: 4

Coverage: 50%

Last proof check: 2026-04-21T20:32:27.774Z

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Subject: Poly-EPO: Training Exploratory Reasoning Models

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Evidence ids

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GitHub Code Pulse

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Last commit

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Claim map

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begins to outperform them as early ask= 32. This improved diversity under repeated sampling is especially valuable in domains such as mathematics
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Ifdita Hasan Orney∗ Jubayer Ibn Hamid∗ Shreya S Ramanujam Shirley Wu Hengyuan Hu Noah Goodman Dorsa Sadigh Chelsea Finn Stanford University ∗Equal contribution. Correspondence to{ifdi1101, jubayer}@stanford.edu
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Figure 5:Training dynamics on synthetic domains. The two left plots correspond tomulti-digit multiplication, andthetworightplotscorrespondtopolynomialsolving
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Furthermore, as test-time compute has become a standard paradigm for performance gains, recent research has explored training objectives aligned with inference-time metrics [TZS+25; CTG+25; CQW+25; WK25]
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overhigh-levelstrategiesduringLMpost-trainingremainsunclear. Similarly,UCBandcount-basedbonuses[SKM25;
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drivingasynergybetweenexplorationandexploitation. Othermethodsimplicitlyinduceexplorationthroughcuriosity- driven techniques [DSL+25; GPW+26] or novel objectives independent of diversity measures [TZZ+26; CQW+25;
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More closely related to our approach are works that promote exploration via objectives targeting the semantic diversity of generations [LZY+25; YCW+25; HWH+26]. However
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the full benefits of polychromic objectives. 8 Conclusion Inthispaper,wepresentedPolychromicExploratoryPolicyOptimization(Poly-EPO)
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2025.url:https://hkunlp.github.io/blog/2025/Polaris. [BBK+24] M.Besta,N.Blach,A.Kubicek,R.Gerstenberger,M.Podstawski,L.Gianinazzi,J.Gajda,T.Lehmann, H. Niewiadomski, P. Nyczyk, and T. Hoefler
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maxθ Ey∼πθ(·|x) [r(x, y) +λd(x, y)], whereπθ is the parameterized policy,r(x, y)is the task reward andd(x, y)is an exploration bonus, such as an entropy bonus, UCB bonus, or semantic diversity bonus. However
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.(2) We can use the policy gradient, whereA(x, y)is the advantage of generationy, to optimize this objective: ∇θEx∼DEy∼πθ(·|x)
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=E x∼DEy∼πθ(·|x) [∇θ logπ θ(y|x)A(x, y)].(3) 2.2 Set Reinforcement Learning Setreinforcementlearning(setRL)[HOX+26]isaframeworkthatgeneralizesstandardRLbyassigningrewardto setsofsampledactionsorgenerations
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