<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rohan Surana | Xunyi's Academic Website</title><link>https://jiangxunyi.github.io/authors/rohan-surana/</link><atom:link href="https://jiangxunyi.github.io/authors/rohan-surana/index.xml" rel="self" type="application/rss+xml"/><description>Rohan Surana</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0000</lastBuildDate><image><url>https://jiangxunyi.github.io/media/icon_hu12864870738162112581.png</url><title>Rohan Surana</title><link>https://jiangxunyi.github.io/authors/rohan-surana/</link></image><item><title>Reasoning Pathologies in Large Language Models: A Diagnostic Perspective</title><link>https://jiangxunyi.github.io/publication/reasoningpathologies/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/reasoningpathologies/</guid><description>&lt;p>A diagnostic study of reasoning failures in large language models.&lt;/p></description></item><item><title>The Geometry of Agent Skills: Non-Commutative Composition in Representation Space</title><link>https://jiangxunyi.github.io/publication/agentskillgeometry/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/agentskillgeometry/</guid><description>&lt;p>A study of the geometry of agent skills and how their composition depends on order in representation space.&lt;/p></description></item><item><title>Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering</title><link>https://jiangxunyi.github.io/publication/selfloops/</link><pubDate>Mon, 20 Jul 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/selfloops/</guid><description>&lt;p>SOPHIA detects when a model repeats unproductive reasoning states and intervenes through hidden-state steering. The method uses transitions between reasoning states to guide generation, improving answer quality and token efficiency.&lt;/p></description></item><item><title>RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts</title><link>https://jiangxunyi.github.io/publication/rrpo/</link><pubDate>Mon, 20 Jul 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/rrpo/</guid><description>&lt;p>RRPO extends group-relative reinforcement learning to tasks without a direct correctness verifier. It compares candidate rollouts against positive and negative reference sets to construct training advantages for reasoning and open-ended generation.&lt;/p></description></item><item><title>Skill-R1: Agent Skill Evolution via Reinforcement Learning</title><link>https://jiangxunyi.github.io/publication/skillr1/</link><pubDate>Sun, 10 May 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/skillr1/</guid><description>&lt;p>Skill-R1 trains a small generator to improve reusable natural-language procedures while keeping the task model fixed. Feedback from successive rollouts guides skill revisions, enabling adaptation with both open and closed models.&lt;/p></description></item><item><title>Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning</title><link>https://jiangxunyi.github.io/publication/rolloutsurvey/</link><pubDate>Wed, 08 Apr 2026 00:00:00 +0000</pubDate><guid>https://jiangxunyi.github.io/publication/rolloutsurvey/</guid><description>&lt;p>This survey organizes rollout design for language-model reinforcement learning into generation, filtering, control, and replay. It connects design choices to reliability, coverage, and computational cost, with examples across reasoning and agent tasks.&lt;/p></description></item></channel></rss>