- TIPS: Turn-level Information-Potential Reward Shaping for Search . . .
We introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple RL framework that assigns dense rewards to each reasoning–tool-call segment based on how much it increases a teacher model’s log-likelihood of the correct answer
- TIPS: Turn-level Information-Potential Reward Shaping for. . .
To address this, we introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn-level rewards to each reasoning + tool-call segment based on the increased likelihood of the correct answer under a teacher model
- TIPS: Turn-level Information-Potential Reward Shaping for Search . . .
TIPS: Turn-level Information-Potential Reward Shaping for Search-Augmented LLMs This repository contains the codebase for TIPS, a verl -based project for Search-R1 style RL training with tool use (multi-turn retrieval, PPO GRPO training, and validation workflows)
- [2605. 04984] Self-Induced Outcome Potential: Turn-Level Credit . . .
We propose Self-Induced Outcome Potential (SIOP), which treats semantic clusters of final answers as latent future outcome states for potential-based turn-level credit assignment
- TIPS: Turn-Level Information-Potential Reward Shaping for Search . . .
To address this, we introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn-level rewards to each reasoning + tool-call segment based on the increased likelihood of the correct answer under a teacher model
- GitHub - ucsd-wang-lab-lm ucsd-wang-lab-lm. github. io: Lab site
This repository hosts the static site for TIPS: Turn-level Information-Potential Reward Shaping for Search-Augmented LLMs The live deployment is served through GitHub Pages from the tips route (the repository root simply redirects there)
- Nicklas Hansen
I am a final-year PhD student at UC San Diego, advised by Professors Xiaolong Wang and Hao Su During my PhD, I have been fortunate to intern at NVIDIA Research and Meta AI (FAIR), and my research has been supported by the NVIDIA Graduate Research Fellowship
- TIPS: Turn-Level Information-Potential Reward Shaping for Search . . .
TIPS solves this by looking at the information potential of each action the model takes When the model searches for something, TIPS evaluates whether that search was likely to find useful information This creates a reward signal during the process, not just at the end
|