Tom Silver

@tomssilver

Assistant Professor @Princeton. Developing robots that plan and learn to help people.

Princeton, NJ

tomsilver.github.io

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Tom Silver
@tomssilver
6h
@BasisOrg and my group at Princeton are recruiting a postdoc to work at the intersection of robotics and code-based world models. We’re looking for someone excited about abstractions & planning, and who knows their way around a real robot. Link 👇 Thanks for boosting!
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Tom Silver
@tomssilver
Sep 13
This week's #PaperILike is "Latent Programming Horizons in Coding Agents" (Silva et al., 2026). Now seems like a good time to better understand what coding agents are doing. Here's a nice example of the kind of analysis one can do. PDF:
arxiv.org
Latent Programming Horizons in Coding Agents
A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the underlying language model internally...
Tom Silver
@tomssilver
Sep 6
This week's #PaperILike is "Safe Model-based Reinforcement Learning with Stability Guarantees" (Berkenkamp et al., NeurIPS 2017). Some safe RL guarantees the learned policy is safe; this guarantees safety *during* learning. Important for RL in real! PDF:
arxiv.org
Safe Model-based Reinforcement Learning with Stability Guarantees
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible...
1
Tom Silver
@tomssilver
Aug 31
We're excited to announce the 4th Workshop on Learning Effective Abstractions for Planning (LEAP) at #CoRL2026! Previous LEAP papers have gone on to win awards at main conferences (SymSkill, Universal Visual Decomposer). Yes, we'll take all the credit! Workshop link 👇
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Tom Silver
@tomssilver
Aug 30
This week's #PaperILike is "Deliberate Practice: Learning Robot Skills under a Budget" (Vats et al., 2026). Practice your robot skills in a provably optimal way. Big fan of this whole line; see also arxiv.org/abs/2209.13605 & arxiv.org/abs/2505.00490 PDF: arxiv.org/abs/2608.13415
arxiv.org
Efficient Recovery Learning using Model Predictive Meta-Reasoning
Operating under real world conditions is challenging due to the possibility of a wide range of failures induced by execution errors and state uncertainty. In relatively benign settings, such...