Iterative reward design repeatedly generates a reward function, trains and evaluates a policy, then revises the reward using behavioral feedback.
Simple choices outperform complex recipes in iterative reward design benchmarks
A unified benchmark finds that only a few design choices reliably improve LLM-assisted reward generation across four reinforcement-learning environment suites.
Big Tech
Logan Mondal Bhamidipaty · Lauren Robson · Linda Petrini · Shengrui Lyu · Kamal Ndousse
Anthropic Fellows Program · University of Edinburgh · Anthropic
Research Digest··3 min read
Thread:RL for Tool Agents
Bhamidipaty and colleagues introduce BIRD, a framework that places iterative reward design methods under matched feedback conditions, implementations and policy-training budgets.
Why this paper
From Anthropic Fellows Program and 2 others · Part of RL for Tool Agents, now 16 papers
In one line
Simple design choices consistently improve iterative reward design, and combining them outperforms prior methods.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ✓Limitations stated by the authors
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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